Doc-Bias Ontology
type: Ontology
imports: dataset characteristic
imports: evaluation measure
imports: core
contributor: Maria-Esther Vidal
contributor: Mayra Russo
contributor: Sammy Sawischa
contributor: Yukti Sharma
creator: Mayra Russo
creator: NoBIAS project
description: Doc-Bias, an ontology to support the documentation of bias in data
created: 2022-06-10
language: en
license: http://creativecommons.org/licenses/by/4.0/
license: GNU General Public License v3.0
publisher: TIB – Leibniz Information Centre for Science and Technology, Hannover, Germany
version info: 0.3
version info: 2023-05-24

creator
type: AnnotationProperty
comment: Mayra Russo

dc:title
type: AnnotationProperty
range: Literal

coversRequirements
type: AnnotationProperty
comment: This annotation property is used for exemplifying possible requirements the content pattern provides a solution for. Requirements are expressed as natural language competency questions.

extractedFrom
type: AnnotationProperty
comment: This annotation property should be assigned with a URI, which points to the possible reference ontology which the annotated pattern was extracted from (i.e. the reference ontology that the ontology elements have been deeply or partially cloned by). The range is not explicit in the definition of the annotation property because it would turn the ontology to OWL Full. E.g. The participation pattern is extracted from the Dolce Ultra Lite ontology, hence the value for this annotation property is http://www.ontologydesignpatterns.org/ont/dul/DUL.owl

hasComponent
type: AnnotationProperty
comment: This annotation property is useful for content ontology design patterns. Its value is a URI, which refers to another content ontology design pattern which is a component of the annotated one.

hasConsequences
type: AnnotationProperty
comment: This annotation property is used for briefly describing the benefits and/or possible trade-offs when using the CP.

hasIntent
type: AnnotationProperty
comment: This annotation property is used in order to describe the intent of the content pattern.

hasUnitTest
type: AnnotationProperty
comment: This property can be used to annotate a unit test (e.g. in the form of a SPARQL query) to be launched to evaluate an ontology against a requirement-based task.

isCloneOf
type: AnnotationProperty
comment: This annotation property is used for referring a cloned ontology entity to its cloning source.

isSpecializationOf
type: AnnotationProperty
comment: This annotation property is useful for content ontology design patterns and its elements. Its value is a URI, which refers either to a content ontology design pattern that is specialized by the annotated one, or to an ontology element that is specialized by the annotated one.

reengineeredFrom
type: AnnotationProperty
comment: This annotation property should be assigned with a URI, which points to the concept schema, page, or anything else from which the annotated pattern was reengineered. If the source does not have any URI e.g., a printed book, this property value should provide information as precise as possible in order to identify the source. This property is alternative to the extractedFrom property because it is used when the pattern come from a concept schema which is not an owl ontology. For example content ontology design patterns, which are reengineered from data model patterns, rdf schemas, etc. should be annotatd with this property. E.g. The basicpersonalfoaf pattern is extracted from the rdf FOAF specification, hence the value for this annotation property is http://xmlns.com/foaf/spec/20071002.rdf

relatedCPs
type: AnnotationProperty
comment: This annotation property can be used to indicate other CPs (if any) that specialize, generalize, inlcude, or are components of the CP. Furthermore, this field may indicate other CPs that are typically used in conjunction with the described one. Important similarities and differences with other patterns can be also described here.

scenarios
type: AnnotationProperty
comment: This annotation property is used for describing examples of instantiation of the Content OP. For example, for the part-of Content OP (which represents part-whole relations) a possible scenario is the sentence: "the brain is part of the human body". Scenarios are expressed as natural language sentences.

creator
type: ObjectProperty
sub property of: topObjectProperty
domain: Dataset
range: Creator
comment: defines creator of the Dataset

communicates_with_language
type: ObjectProperty
sub property of: topObjectProperty
range: Language

has biological sex
type: ObjectProperty
sub property of: topObjectProperty
type: FunctionalProperty
domain: Person
range: Biological Sex
comment: association of gender identity concept with a biological gender

has_skin_color
type: ObjectProperty
sub property of: topObjectProperty
type: FunctionalProperty
domain: Person
range: Skin Color

identifies_with_religion
type: ObjectProperty
sub property of: topObjectProperty
domain: Religious Identity
range: Religion

is_of_ethnic_origin
type: ObjectProperty
sub property of: topObjectProperty
domain: Ethnic Identification
range: Ethnic Origin

is_of_the_racial_background
type: ObjectProperty
sub property of: topObjectProperty
domain: Person
range: Race

self_identifies_gender_as
type: ObjectProperty
sub property of: topObjectProperty
domain: Person
range: Gender Self-Identification

theme
type: ObjectProperty
sub property of: topObjectProperty
domain: Dataset
range: Theme
comment: A main category of the resource. A resource can have multiple themes.

computedOn
type: ObjectProperty
sub property of: topObjectProperty
domain: QualityMeasurement
range: Dataset
comment: defines measure of metric which is computed on dataset

hasQualityAnnotation
type: ObjectProperty
sub property of: topObjectProperty
domain: Dataset
range: QualityAnnotation
comment: defines annotations related to quality that Dataset has

inCategory
type: ObjectProperty
sub property of: topObjectProperty
domain: QualityDimension
range: Category
comment: defines Category that given Dimension belongs to

inDemension
type: ObjectProperty
sub property of: topObjectProperty
domain: QualityMetric
range: QualityDimension
comment: defines Dimension who has given Metric

isMeasurementOf
type: ObjectProperty
sub property of: topObjectProperty
domain: QualityMeasurement
range: QualityMetric
comment: defines metric which is measured

achieves
type: ObjectProperty
sub property of: topObjectProperty

definedOn
type: ObjectProperty
sub property of: topObjectProperty

defines
type: ObjectProperty
sub property of: topObjectProperty

executes
type: ObjectProperty
sub property of: topObjectProperty
domain: Run

hasInput
type: ObjectProperty
sub property of: topObjectProperty
domain: Run
range: Dataset

hasPart
type: ObjectProperty
sub property of: topObjectProperty
range: EvaluationProcedure

hasQuality
type: ObjectProperty
sub property of: topObjectProperty

implements
type: ObjectProperty
sub property of: topObjectProperty
range: Algorithm

realizes
type: ObjectProperty
sub property of: topObjectProperty

specifiedBy
type: ObjectProperty
sub property of: topObjectProperty
comment: A relation between an entity and the information content entity that specifies it.

target
type: ObjectProperty
sub property of: topObjectProperty
domain: QualityPolicy
range: Dataset
comment: defines dataset for which a quality policy is applicable for

wasAttributedTo
type: ObjectProperty
sub property of: topObjectProperty
domain: BiasMeasure
range: ScholarlyDocument
comment: attributing provenance of Bias to a scholarly document.

aligns
type: ObjectProperty
sub property of: topObjectProperty
inverse of: isAlignedWith
domain: Harm
range: Bias

associatedTo
type: ObjectProperty
sub property of: topObjectProperty
inverse of: composedOf
domain: DemographicDimension
range: Dataset

associates
type: ObjectProperty
sub property of: topObjectProperty
inverse of: isAssociatedTo
domain: Application
range: Bias

composedOf
type: ObjectProperty
sub property of: topObjectProperty
domain: Dataset
range: DemographicDimension

evaluatesFeature
type: ObjectProperty
sub property of: topObjectProperty
domain: BiasEvaluation
range: Feature

evaluatesForTask
type: ObjectProperty
sub property of: topObjectProperty
domain: BiasEvaluation
range: Task

evaluatesInDataset
type: ObjectProperty
sub property of: topObjectProperty
domain: BiasEvaluation
range: Dataset

evaluatesWithMeasure
type: ObjectProperty
sub property of: topObjectProperty
domain: BiasEvaluation
range: BiasMeasure

hasBiasMeasure
type: ObjectProperty
sub property of: topObjectProperty
inverse of: measures
domain: Bias
range: BiasMeasure

hasDataset
type: ObjectProperty
sub property of: topObjectProperty
range: Dataset

hasDocumentationType
type: ObjectProperty
sub property of: topObjectProperty
domain: Dataset
domain: BiasEvaluation
domain: ModelComponent
range: DocumentationType

hasModelComponent
type: ObjectProperty
sub property of: topObjectProperty
domain: Model
range: ModelComponent

hasOutput
type: ObjectProperty
sub property of: topObjectProperty
domain: Run
range: Model
range: ModelEvaluation

isAlignedWith
type: ObjectProperty
sub property of: topObjectProperty
domain: Bias
range: Harm

isAssociatedTo
type: ObjectProperty
sub property of: topObjectProperty
domain: Bias
range: Application

isPartOf
type: ObjectProperty
sub property of: topObjectProperty

measures
type: ObjectProperty
sub property of: topObjectProperty
domain: BiasMeasure
range: Bias

preparesData
type: ObjectProperty
sub property of: topObjectProperty
domain: DataPre-processing
range: Dataset
comment: defines files that are used in preparing the Data for training to be used by Model

Issued
type: DatatypeProperty
domain: Dataset
range: Literal
comment: defines date on which Dataset is published

description
type: DatatypeProperty
domain: Dataset
range: Literal
comment: gives description of dataset

modified
type: DatatypeProperty
domain: Dataset
range: Literal
comment: defines date on which Dataset is modified

title
type: DatatypeProperty
domain: Dataset
range: Literal
comment: defines title of the Dataset

Keyword
type: DatatypeProperty
domain: Dataset
range: Literal
comment: defines Keywords describing the Dataset

ExpectedDatatype
type: DatatypeProperty
domain: QualityMetric
range: Literal
comment: defines expected datatype of metric

qualityValue
type: DatatypeProperty
domain: QualityMeasurement
range: Literal
comment: defines value of the metric measured

age
type: DatatypeProperty
domain: Feature
range: int

biasClassification
type: DatatypeProperty
domain: BiasEvaluation
range: Literal

biasMeasureID
type: DatatypeProperty
domain: BiasMeasure
range: Literal

biasScore
type: DatatypeProperty
domain: BiasEvaluation
range: Literal
comment: Represents the bias score calculated with bias detection method

biasThreshold
type: DatatypeProperty
domain: BiasEvaluation
range: int

datasetName
type: DatatypeProperty
domain: Dataset
range: string
creator: russo

evaluationID
type: DatatypeProperty
domain: BiasEvaluation
range: Literal

featureSubGroup
type: DatatypeProperty
domain: BiasEvaluation
range: Literal

hasContentType
type: DatatypeProperty
domain: Dataset
range: Literal

hasMeasureValue
type: DatatypeProperty
domain: EvaluationMeasure
range: float
comment: hssMeasureValue

hasValue
type: DatatypeProperty
domain: EvaluationMeasure
range: decimal

isAvailable
type: DatatypeProperty
domain: Dataset
range: boolean
comment: defines whether dataset is publicly available

modelName
type: DatatypeProperty
domain: Model
range: string
creator: russo

privFeature
type: DatatypeProperty
domain: BiasEvaluation
range: Literal
comment: sub-group in feature assessed is considered to be privileged when in comparison with another sub-group.

specificationDescription
type: DatatypeProperty
domain: EvaluationProcedure
range: string
comment: specificationDescription

spontaneity
type: DatatypeProperty
domain: Dataset
range: boolean
comment: defines whether dataset was spontaneously collected from a Social Media Platform or computer-generated

underPrivFeature
type: DatatypeProperty
domain: BiasEvaluation
range: Literal
comment: sub-group in feature assessed is considered to be underprivileged when in comparison with another sub-group.

valueCount
type: DatatypeProperty
domain: BiasEvaluation
range: int

SocioEconomicStatus
type: Class
subclass of: DemographicDimension
comment: An economic and sociological combined total measure of a person's work experience and of an individual's or family's economic and social position in relation to others, based on household income, earners' education, and occupation are examined, as well as combined income, whereas for an individual's SES only their own attributes are assessed. However, SES is more commonly used to depict an economic difference in society as a whole.

Alaskan Native
type: Class
subclass of: Race
comment: "A person having origins in any of the original peoples of North and South America (including Central America), and who maintains tribal affiliation or community attachment." OMB Directive 15

Algerian
type: Class
subclass of: Ethnic Self Identity

American Indian
type: Class
subclass of: Race
comment: "A person having origins in any of the original peoples of North and South America (including Central America), and who maintains tribal affiliation or community attachment." OMB Directive 15

Asexual
type: Class
subclass of: Sexual Orientation
comment: lacking in sexual attraction and interest

Asian
type: Class
subclass of: Race
comment: "A person having origins in any of the original peoples of the Far East, Southeast Asia, or the Indian subcontinent including, for example, Cambodia, China, India, Japan, Korea, Malaysia, Pakistan, the Philippine Islands, Thailand, and Vietnam." OMB Directive 15, 1997

Aztec
type: Class
subclass of: Ethnic Self Identity

Barbadian
type: Class
subclass of: Ethnic Self Identity

Bisexual
type: Class
subclass of: Sexual Orientation
comment: sexual interest in both biological sex, males and females

Black
type: Class
subclass of: Race
comment: "A person having origins in any of the black racial groups of Africa. Terms such as "Haitian" or "Negro" can be used in addition to "Black or African American."" OMB Directive 15, 1997

Blackfeet Tribe
type: Class
subclass of: Ethnic Self Identity

Cambodian
type: Class
subclass of: Ethnic Self Identity

Chamorro
type: Class
subclass of: Ethnic Self Identity

Chinese
type: Class
subclass of: Ethnic Self Identity

Chuukese
type: Class
subclass of: Ethnic Self Identity

Columbian
type: Class
subclass of: Ethnic Self Identity

Cuban
type: Class
subclass of: Ethnic Self Identity

Dominican
type: Class
subclass of: Ethnic Self Identity

Dutch
type: Class
subclass of: Ethnic Self Identity

Ecuadorian
type: Class
subclass of: Ethnic Self Identity

Egyptian
type: Class
subclass of: Ethnic Self Identity

English
type: Class
subclass of: Ethnic Self Identity

Ethiopian
type: Class
subclass of: Ethnic Self Identity

Ethnic Identification
type: Class
subclass of: DemographicDimension
subclass of: Class
  intersection of: Restriction
    on property: has ethnic identity
    some values from: Ethnic Identity
  intersection of: Restriction
    on property: has ethnic origin
    some values from: Ethnic Origin
  intersection of: Restriction
    on property: has religious identifcation
    some values from: Religious Identity
comment: concept expresses the ethnic identification of the ego where it encompasses ethnic origin, how they identify with their ethnicity and their religious idenity affiliation. For example, individuals who have mutliple ethnic origins ("mixed race"), may identify strongly with one than the other, or individuals from multi-faceted backgrounds (e.g. "Afro Cuban") may self identify as Latino, etc.

Ethnic Identity
type: Class
subclass of: DemographicDimension
comment: representation of various facets of self ethnic idenity - strength of association and what they identify

Ethnic Origin
type: Class
subclass of: DemographicDimension
comment: We are utilizing the Office of Management and Budget (OMB) classifcation for ethnicity. While not comprehensive, it is temporary, but we have added some subclasses to these.

Ethnic Self Identity
type: Class
subclass of: DemographicDimension
comment: Individual association with an ethnic group, independent of their origin. This mirrors Ethnic Origin classification of FOAF+.

Fijian
type: Class
subclass of: Ethnic Self Identity

Filipino
type: Class
subclass of: Ethnic Self Identity

French
type: Class
subclass of: Ethnic Self Identity

Gender Self-Identification
type: Class
subclass of: DemographicDimension
comment: Individual association with a gender group, independent of their biological gender. This mirrors Gender classification of FOAF+.

German
type: Class
subclass of: Ethnic Self Identity

Ghanaian
type: Class
subclass of: Ethnic Self Identity

Guatemalan
type: Class
subclass of: Ethnic Self Identity

Haitian
type: Class
subclass of: Ethnic Self Identity

Hawaiian
type: Class
subclass of: Ethnic Self Identity

Hetrosexual
type: Class
subclass of: Sexual Orientation
comment: sexual attraction and interest in the opposite biological sex

Hispanic
type: Class
subclass of: Race
comment: "A person of Cuban, Mexican, Puerto Rican, South or Central American, or other Spanish culture or origin, regardless of race. The term, "Spanish origin," can be used in addition to "Hispanic or Latino."" OMB Directive 15, 1997
alternative label: Latino

Hmong
type: Class
subclass of: Ethnic Self Identity

Homosexual
type: Class
subclass of: Sexual Orientation
comment: sexual attraction and interest in the same biological sex

Indian
type: Class
subclass of: Ethnic Self Identity

Intersex
type: Class
subclass of: Biological Sex
editorial note: "do not fit the typical definitions for male or female bodies"

Iranian
type: Class
subclass of: Ethnic Self Identity

Iraqi
type: Class
subclass of: Ethnic Self Identity

Irish
type: Class
subclass of: Ethnic Self Identity

Israeli
type: Class
subclass of: Ethnic Self Identity

Italian
type: Class
subclass of: Ethnic Self Identity

Jamaican
type: Class
subclass of: Ethnic Self Identity

Japanese
type: Class
subclass of: Ethnic Self Identity

Korean
type: Class
subclass of: Ethnic Self Identity

Language
type: Class
subclass of: DemographicDimension
comment: concept classification of various spoken languages, not an exhaustive list but limited to the languages presented in YMAP and ADD Health surveys

Lebanese
type: Class
subclass of: Ethnic Self Identity

Marshallese
type: Class
subclass of: Ethnic Self Identity

Mayan
type: Class
subclass of: Ethnic Self Identity

Moroccan
type: Class
subclass of: Ethnic Self Identity

Native Hawaiian
type: Class
subclass of: Race
comment: "A person having origins in any of the original peoples of Hawaii, Guam, Samoa, or other Pacific Islands." OMB Directive 15, 1997

Native Village of Barrow Inupiat
type: Class
subclass of: Ethnic Self Identity

Navajo Nation
type: Class
subclass of: Ethnic Self Identity

Nigerian
type: Class
subclass of: Ethnic Self Identity

Nome Eskimo Community
type: Class
subclass of: Ethnic Self Identity

Norweigian
type: Class
subclass of: Ethnic Self Identity

Pacific Islander
type: Class
subclass of: Race
comment: "A person having origins in any of the original peoples of Hawaii, Guam, Samoa, or other Pacific Islands." OMB Directive 15, 1997

Pakistani
type: Class
subclass of: Ethnic Self Identity

Palauan
type: Class
subclass of: Ethnic Self Identity

Pansexual
type: Class
subclass of: Sexual Orientation
comment: attraction to many/any genders beyond the binary classification

Polish
type: Class
subclass of: Ethnic Self Identity

Puerto Rican
type: Class
subclass of: Ethnic Self Identity

Race
type: Class
subclass of: DemographicDimension
comment: individual racial background based on 1997 Office of Management and Budget (OMB) classification

Religion
type: Class
subclass of: DemographicDimension
comment: concept classification of world religions that individuals may adhere to

Religious Adherance
type: Class
subclass of: DemographicDimension
comment: degree of adherence to religious belief for an individual based on scales used in surveys from YMAP and ADD Health

Religious Identity
type: Class
subclass of: DemographicDimension
subclass of: Class
  intersection of: Restriction
    on property: has religious adherance
    some values from: Religious Adherance
  intersection of: Restriction
    on property: identifies_with_religion
    some values from: Religion
comment: A concept that is part of the ethnic identification concept that extends to religious identity and how much they adhere to it

Salvadoran
type: Class
subclass of: Ethnic Self Identity

Samoan
type: Class
subclass of: Ethnic Self Identity

Scottish
type: Class
subclass of: Ethnic Self Identity

Female
type: Class
subclass of: Gender Self-Identification

Gender Queer
type: Class
subclass of: Gender Self-Identification

Male
type: Class
subclass of: Gender Self-Identification

Transgender
type: Class
subclass of: Gender Self-Identification

Sexual Orientation
type: Class
subclass of: DemographicDimension
comment: a concept referring to the biological gender an individual is sexually attracted to

Skin Color
type: Class
subclass of: DemographicDimension
comment: Skin color classification based on Fitzpatrick scale

South African
type: Class
subclass of: Ethnic Self Identity

Spaniard
type: Class
subclass of: Ethnic Self Identity

Syrian
type: Class
subclass of: Ethnic Self Identity

Tahitian
type: Class
subclass of: Ethnic Self Identity

Tongan
type: Class
subclass of: Ethnic Self Identity

Tunisian
type: Class
subclass of: Ethnic Self Identity

Vietnamese
type: Class
subclass of: Ethnic Self Identity

White
type: Class
subclass of: Race
comment: "A person having origins in any of the original peoples of Europe, the Middle East, or North Africa." OMB Directive 15
alternative label: Caucasian

Person
type: Class
subclass of: Thing

SpatialThing
type: Class
subclass of: Thing

Collection
subclass of: Thing

Theme
type: Class
subclass of: Thing
comment: Describes the theme of a dataset.

Concept Scheme
subclass of: Thing

Catalog
type: Class
subclass of: Dataset
comment: A curated collection of metadata about resources (e.g., datasets and data services in the context of a data catalog).

Dataset
type: Class
equivalent class: Dataset
subclass of: Thing
comment: A collection of data, published or curated by a single source, and available for access or download in one or more representations.

Category
type: Class
subclass of: Thing
comment: Category is a group of quality dimensions in which a common type of information is used as quality indicator

QualityDimension
type: Class
subclass of: Thing
comment: Dimension is a criteria relevant for assessing quality. Each dimension is part of a broader category of Data Quality Assessment

QualityMetric
type: Class
subclass of: Thing
comment: Metric is a standard to measure a quality dimension. An observation(instance of dqv:QualityMeasurement) assigns a value in a given unit to a Metric.

QualityAnnotation
type: Class
subclass of: Thing
comment: QualityAnnotation is a quality annotations, including ratings, quality certificates or feedback that can be associated to datasets

QualityCertificate
type: Class
subclass of: QualityAnnotation
comment: Quality Certificate is subclass of Quality Annotation. It associates a dataset to a document that certifies the data quality according to a set of quality assessment rules

QualityMeasurement
type: Class
subclass of: Thing
comment: Quality Measurement is the evaluation of a given dataset a specific quality metric.

QualityPolicy
type: Class
subclass of: Thing
comment: QualityPolicy is a policy or agreement that is chiefly governed by data quality concerns

Algorithm
type: Class
subclass of: InformationEntity
comment: An optimization algorithm in machine learning problems minimizes a loss or cost function by systematically choosing input values from within an allowed set and computing the value of the function.

Data
type: Class
subclass of: InformationEntity
subclass of: Restriction
  on property: hasQuality
  some values from: DataCharacteristic
comment: Data is a data item composed of data examples and it may be of a various level of granularity and complexity. With regard to granularity, it can be a whole dataset (for instance, one main table and possibly other tables), or only a single table, or only a feature (e.g., a column of a table), or only an instance (e.g., row of a table), or a single feature-value pair. With regard to complexity, data examples are characterized by their datatype, which may be arbitrarily complex (e.g., instead of a table it can be an arbitrary graph).

DataCharacteristic
type: Class
subclass of: InformationEntity
comment: Data Characteristic is a distinguishing quality or property that distinguish one data from another. Such characteristics are often statistical ones (e.g., the number of instances or the number of features of a data set). They may be also informationtheoretic measures (e.g., class entropy of a categorical data set) or geometric measures of data complexity (e.g., the highest discriminatory power of any single feature in the data set).

Dataset
type: Class
subclass of: Data
definition: A collection of data, published or curated by a single source, and available for access or download in one or more representations.

EvaluationMeasure
type: Class
subclass of: InformationEntity
creator: russo
comment: EvaluationMeasure is a measure to assess the performance of the model generated by the process that realizes the task. Examples are predictive accuracy or f-measure.

EvaluationProcedure
type: Class
subclass of: InformationEntity
comment: EvaluationProcedure is a technique to evaluate machine learning models. Examples are cross-validation and leave-one-out.

EvaluationSpecification
type: Class
subclass of: InformationEntity
subclass of: Restriction
  on property: hasPart
  some values from: EvaluationMeasure
subclass of: Restriction
  on property: hasPart
  some values from: EvaluationProcedure
comment: EvaluationSpecification is a specification of an evaluation measure and an evaluation procedure to assess the performance of the process that realizes the task.

Experiment
type: Class
subclass of: Process
subclass of: Restriction
  on property: hasPart
  some values from: Run
comment: The training process of fake news detection model consists of several experiments that are subclasses of the Process and they are part of a Study(research project). Each Experiment is done for a specific goal decided by the study it is part of.

Feature
type: Class
subclass of: Data

Implementation
type: Class
subclass of: InformationEntity

InformationEntity
type: Class
subclass of: Thing

Model
type: Class
subclass of: InformationEntity
subclass of: Restriction
  on property: hasOutput
  some values from: Output
comment: Model is a generalization of a set of training data able to predict values for unseen instances. It is an output from an execution of a data mining algorithm implementation. Models have a dual nature. They can be treated as data structures and as such represented, stored and manipulated. On the other hand, they act as functions and are executed, taking as input data examples and giving as output the result of applying the function to a data example. Models can also be divided into global or local ones. A global model has global coverage of a data set, i.e., it generalizes the whole data set. A local model, such as a pattern set, is a set of local hypotheses, i.e. each applies to a limited region of the data set.

ModelEvaluation
type: Class
subclass of: InformationEntity
subclass of: Restriction
  on property: specifiedBy
  some values from: EvaluationMeasure
subclass of: Restriction
  on property: hasDocumentationType
  some values from: LearningDocumentation
comment: ModelEvaluation is a setting of a value of the performance measure specified by the evaluation specification. It connects a measure specification with its value.

Process
type: Class
subclass of: Thing
comment: This class represents the process followed in a classification problem using a particular machine learning model.

Run
type: Class
subclass of: Process
subclass of: Restriction
  on property: hasInput
  some values from: Dataset
subclass of: Restriction
  on property: realizes
  some values from: Algorithm
subclass of: Restriction
  on property: hasOutput
  some values from: Model
subclass of: Restriction
  on property: hasOutput
  some values from: ModelEvaluation
comment: Run is an execution of an implementation on a machine (computer). It is limited in time (has a start and end point), can be successful or failed.

Study
type: Class
subclass of: Process
subclass of: Restriction
  on property: hasPart
  some values from: Experiment
comment: Study is a collection of runs that belong together to do some kind of analysis on its results. This analysis can be general or very specific (e.g. a hypothesis test). Can be linked to files, data, that belong to it.

Task
type: Class
subclass of: InformationEntity
subclass of: Restriction
  on property: definedOn
  some values from: Dataset
subclass of: Restriction
  on property: defines
  some values from: EvaluationSpecification
comment: Task is a formal description of a process that needs to be completed (e.g. based on inputs and outputs). A Task is any piece of work that needs to be addressed in the data mining process. In ML Schema, it is defined based on data.

Creator
type: Class
subclass of: Thing
comment: Represents the creator of the dataset.

ScholarlyDocument
type: Class
subclass of: Thing
comment: Scholarly document that represents the provenance where the bias measure has been defined.

Age Bias
type: Class
subclass of: Demography Bias

Aggregation Bias
type: Class
equivalent class: Ecological Fallacy Bias
subclass of: Selection And Sampling Bias
definition: Arises when false conclusions are drawn about individuals from observing the entire population.

Algorithmic Bias
type: Class
subclass of: Computational Bias
definition: Algorithmic bias is when the bias is not present in the input data and is added purely by the algorithm

Alienation
type: Class
subclass of: Harm
definition: Denial of the relevance of socially meaningful categories.

Application
type: Class
subclass of: Thing
disjoint with: Bias
definition: The use, purpose or appliation of artificial intelligence technology.

Bias
type: Class
equivalent class: Bias
subclass of: Thing
subclass of: Restriction
  on property: hasBiasMeasure
  some values from: BiasMeasure
subclass of: Restriction
  on property: isAlignedWith
  some values from: Harm
subclass of: Restriction
  on property: isAssociatedTo
  some values from: Application
disjoint with: BiasMeasure
disjoint with: Harm
citation: Pitoura, E. 2020. Social-minded Measures of Data Quality: Fairness, Diversity, and Lack of Bias. J. Data and Information Quality 12, 3, Article 12 (July 2020), 8 pages.
definition: A concentration on, or interest in one particular area, or subject.
definition: Bias is an inclination, or prejudice for, or against one person, or group, especially in a way considered to be unfair.

BiasDocumentation
type: Class
subclass of: DocumentationType
label: This class represents the stage in the documentation pipeline that pertains to the capture of bias knowledge.

BiasEvaluation
type: Class
subclass of: Thing
comment: This class represents the Evaluation property.

BiasMeasure
type: Class
subclass of: Thing
type: NamedIndividual
type: BiasEvaluation
definition: Bias measure corresponds to a quantitative metric or indicator that assesses the presence and extent of bias in a particular context.
comment: Bias Measure represents the measure defined in some scholarly document to measure bias detected in data in association to a bias.

DataCleaning
type: Class
subclass of: DataPre-processing
comment: method used for data cleaning

DataIntegration
type: Class
subclass of: DataPre-processing
comment: method used for data integration

DataTransformation
type: Class
subclass of: DataPre-processing
comment: method used for data transformation

DataReduction
type: Class
subclass of: DataPre-processing
comment: method used for data reduction

DatasetDocumentation
type: Class
subclass of: DocumentationType
label: This class represents the stage in the documentation pipeline that pertains to the capture of knowledge and metadata pertaining to the dataset.

Dehumanization
type: Class
subclass of: Harm
definition: The viewing and the treatment of other people as though they lack the mental capacities that are commonly attributed to human beings.

DemographicDimension
type: Class
subclass of: Feature
comment: An identity axis (e.g., gender, race, age) for which specific instances (e.g., for gender: male, female, non-binary, etc) are evaluated. Instances of a demographic dimension are typically comparatively evaluated in measures through some proxy, e.g., occupations or identity terms.
is defined by: On Measures of Biases and Harms in NLP,https://aclanthology.org/2022.findings-aacl.24, Dev et al., Findings 2022

Demography Bias
type: Class
subclass of: Societal Bias
subclass of: Restriction
  on property: isAlignedWith
  some values from: Dehumanization
subclass of: Restriction
  on property: isAlignedWith
  some values from: Stereotyping
definition: Engaging in discriminatory behavior, holding negative attitudes toward, or otherwise having less favorable reactions toward people based on a demographic factor.

Denigration
type: Class
subclass of: Harm
definition: A harm that arises when a system applies a label that has a long history of being purposefully used to denigrate and demean people.

Direct Discrimination
type: Class
subclass of: Unexplainable Discrimination

Discrimination
type: Class
definition: Discrimination can be considered as a source for unfairness that is due to human prejudice and stereotyping based on the sensitive attributes, which may happen intentionally or unintentionally.

Disparagement
type: Class
subclass of: Harm
definition: Disparagement encapsulates any behaviour by a model which reinforces the notion that certain groups are less valuable than others and less deserving of respect or resources.

DocumentationType
type: Class
subclass of: Thing
comment: This class represents the stage in the documentation pipeline.

Erasure
type: Class
equivalent class: SocialInvisibility
subclass of: Harm
definition: Erasure or social invisibility refers to a group of people in society that can be excluded or systematically ignored from resource allocation procedures or decision-making processes due to data collection practices or historical record keeping.
definition: Erasure refers to the lack of adequate representation of members of a particular social group, intentional or not.

Ethnicity Bias
type: Class
subclass of: Demography Bias
definition: When models are partial towards certain ethnicity.

Explainable Discrimination
type: Class
subclass of: Discrimination

Extrinsic Bias
type: Class
subclass of: Computational Bias

Gender Bias
type: Class
subclass of: Societal Bias
definition: Gender bias is the tendency to give preferential treatment to one gender over another.
note: Identification as male/masculine, female/feminine or something else, and association with a (social) role or set of behavioral and cultural traits, clothing, etc; a category to which a person belongs on this basis. Gender is the result of a complex combination of gender role, gender expression, gender identity, and gender modality. http://purl.obolibrary.org/obo/NCIT_C17357

Gender Bias Measure
type: Class
subclass of: Socio-Demographic Bias Measure

Harm
type: Class
subclass of: Thing
is defined by: Renee Shelby, Shalaleh Rismani, Kathryn Henne, AJung Moon, Negar Rostamzadeh, Paul Nicholas, N’Mah Yilla-Akbari, Jess Gallegos, Andrew Smart, Emilio Garcia, and Gurleen Virk. 2023. Sociotechnical Harms of Algorithmic Systems: Scoping a Taxonomy for Harm Reduction. In AAAI/ACM Conference on AI, Ethics, and Society (AIES ’23). August 8–10, 2023, Montréal,QC, Canada.
definition: Adverse lived experiences resulting from an AI system’s deployment and operation in the world.

HatespeechDetection
type: Class
subclass of: SocialNLPLModel

Homophily
type: Class
subclass of: Group Bias
subclass of: Restriction
  on property: isAssociatedTo
  some values from: RecommenderSystem
comment: Predominantly, studied in the context of social network structure and the output of ranking algorithms.
definition: The tendency to connect to similar users or people as oneself.

Indirect Discrimination
type: Class
subclass of: Unexplainable Discrimination

Inductive Bias
type: Class
equivalent class: Learning Bias
subclass of: Computational Bias

Intrinsic Bias
type: Class
subclass of: Computational Bias

Learning Bias
type: Class
subclass of: Computational Bias

LearningDocumentation
type: Class
subclass of: DocumentationType
comment: This class represents the stage in the documentation pipeline that pertains to the capture of knowledge and metadata pertaining to the learning and training process.

Linking Bias Measure
type: Class
subclass of: BiasMeasure

Longitudinal Data Fallacy
type: Class
subclass of: Use And Interpretation Bias
definition: Researchers analyzing temporal data must use longitudinal analysis to track cohorts over time to learn their behavior. Instead, temporal data is often modeled using cross-sectional analysis, which combines diverse cohorts at a single time point. The heterogeneous cohorts can bias cross-sectional analysis, leading to different conclusions than longitudinal analysis.

MachineTranslation
type: Class
subclass of: SocialNLPLModel
comment: Automatically translate spoken language and textual content.

MeasuresBiasIn
type: Class
subclass of: Restriction
  on property: hasDocumentationType
  some values from: DocumentationType

Modifiable Areal Unit Problem
type: Class
subclass of: Ecological Fallacy Bias
definition: A statistical bias in geospatial analysis, which arises when modeling data at different levels of spatial aggregation. This bias results in different trends learned when data is aggregated at different spatial scales.

NamedEntityRecognitionSystem
type: Class
subclass of: SocialNLPLModel

Nationality Bias
type: Class
subclass of: Societal Bias
subclass of: Restriction
  on property: isAssociatedTo
  some values from: SocialNLPLModel
definition: Engaging in discriminatory behavior, holding negative attitudes toward, or otherwise having less favorable reactions toward people based on their nationality.

Nationality Bias Measure
type: Class
subclass of: Socio-Demographic Bias Measure
subclass of: Restriction
  on property: measures
  some values from: Nationality Bias

Omitted Variable Bias
type: Class
subclass of: Selection And Sampling Bias
definition: Occurs when one or more important variables are left out of the model.

Popularity Bias Measure
type: Class
subclass of: BiasMeasure

DataPre-processing
type: Class
subclass of: Thing
comment: Pre-processing class defines the method by which Pre-procesing is done. Typically it stores the files used for data pre-processing. There are many ways to do pre-processing like data reduction, data cleaning, data integration and data transformation which are the subclasses of Pre-processing class.

Proxy Discrimination
type: Class
subclass of: Discrimination
is defined by: Niki Kilbertus, Mateo Rojas Carulla, Giambattista Parascandolo, Moritz Hardt, Dominik Janzing, and Bernhard Schölkopf. 2017. Avoiding discrimination through causal reasoning. In Proceedings of the International Conference on Advances in Neural Information Processing Systems.
definition: A variable V in a causal graph exhibits potential proxy discrimination, if there exists a directed path from A to V that is blocked by a proxy variable and V itself is not a proxy.

PublicParticipation
type: Class
subclass of: Harm
definition: When athe implementation of a systems results in diminishing the ability of a particular population to participate in public discourse.

QualityOfService
type: Class
subclass of: Harm
definition: Quality of service harms result from instances when a model fails to perform equitably for different groups.

Race Bias
type: Class
subclass of: Demography Bias
alternative label: Racism
definition: When models are biased against certain races.

Ranking Bias
type: Class
subclass of: Use And Interpretation Bias
subclass of: Restriction
  on property: associatedTo
  some values from: CrowdsourcingApplication
subclass of: Restriction
  on property: associatedTo
  some values from: RecommenderSystem
subclass of: Restriction
  on property: associatedTo
  some values from: SearchEngine
definition: The idea that top-ranked results are the most relevant and important will result in attraction of more clicks than others.

RecommenderSystem
type: Class
subclass of: Application
comment: Recommender system's are widely used in e-commerce platforms, i.e., Amazon, Netflix, to generate product or content recommendations.
definition: A recommendation system is developed with the objective of predicting the preference of a system's user for a particular item.

Reporting Bias
type: Class
equivalent class: Measurement Bias
subclass of: Selection And Sampling Bias
definition: Arises from how we choose, utilize, and measure particular features

Representation
type: Class
subclass of: Harm
definition: Those harms which arise when a system represents some social groups in a less favorable light than others, demeans them, or fails to recognize their existence altogether.

Representation Bias Measure
type: Class
subclass of: BiasMeasure
subclass of: Restriction
  on property: measures
  some values from: Representation Bias

Self-SelectionBias
type: Class
subclass of: Selection And Sampling Bias
definition: When the sampling method only reaches a portion of the population or the population of interest has changed or is distinct from the population used during model training.

Sex bias
type: Class
subclass of: Demography Bias
alternative label: Sexism
definition: Prejudice or discrimination based on one's sex or gender.
note: Sex is, according to medical databases, classification of studied subjects as male or female according to their reproductive organs and functions assigned by sets of chromosomes. Sex is related to reproductive organs anatomy and physiology. http://purl.obolibrary.org/obo/NCIT_C28421

Simpsons Paradox Measure
type: Class
subclass of: BiasMeasure

SocialInvisibility
type: Class
subclass of: Harm
definition: Erasure or social invisibility refers to a group of people in society that can be excluded or systematically ignored from resource allocation procedures or decision-making processes due to data collection practices or historical record keeping.

SocialNLPLModel
type: Class
subclass of: Application
comment: NLP tasks break down human text and voice data to support the ability of computers to make sense of what it is ingesting.

Socio-Demographic Bias Measure
type: Class
subclass of: BiasMeasure

Statistical Discrimination
type: Class
subclass of: Discrimination

Stereotyping
type: Class
subclass of: Harm
definition: Stereotypes are overgeneralized beliefs about the personal attributes of an individual as determined by their demographic group membership.

Stigmatization
type: Class
subclass of: Harm
definition: When systems e.g., NLP systems, treat some language varieties or practices as less grammatical, more offensive, or less appropriate for public consumption in comparison to others. This reproduces dominant language ideolegies.

Systemic Discrimination
type: Class
subclass of: Discrimination

UnderlyingDistributionSkew
type: Class
subclass of: Selection And Sampling Bias
definition: The underlying distribution that data is collected from may lack an equal ratio or sufficient representation for all of its subpopulations. In such cases, the underlying distribution is inherently skewed, and there are no discriminatory motives behind it.

Unexplainable Discrimination
type: Class
subclass of: Discrimination

Unresolved Discrimination
type: Class
subclass of: Discrimination
is defined by: Niki Kilbertus, Mateo Rojas Carulla, Giambattista Parascandolo, Moritz Hardt, Dominik Janzing, and Bernhard Schölkopf. 2017. Avoiding discrimination through causal reasoning. In Proceedings of the International Conference on Advances in Neural Information Processing Systems.
definition: A variable V in a causal graph exhibits unresolved discrimination if there exists a directed path from A to V that is not blocked by a resolving variable, and V itself is non-resolving

Activity Bias
type: Class
subclass of: Use And Interpretation Bias
IAO_0000115: A type of selection bias that occurs when systems/platforms get their training data from their most active users, rather than those less active (or inactive).

Axiom
annotated source: Activity Bias
annotated property: IAO_0000115
annotated target: A type of selection bias that occurs when systems/platforms get their training data from their most active users, rather than those less active (or inactive).
has db xref: https://doi.org/10.6028/NIST.SP.1270

Amplification Bias
type: Class
subclass of: Processing Bias
IAO_0000115: Arises when the distribution over prediction outputs is skewed in comparison to the prior distribution of the prediction target.

Axiom
annotated source: Amplification Bias
annotated property: IAO_0000115
annotated target: Arises when the distribution over prediction outputs is skewed in comparison to the prior distribution of the prediction target.
has db xref: https://doi.org/10.6028/NIST.SP.1270

Anchoring Bias
type: Class
subclass of: Individual Bias
IAO_0000115: A cognitive bias, the influence of a particular reference point or anchor on people’s decisions. Often more fully referred to as anchoring-and-adjustment, or anchoring-and-adjusting: after an anchor is set, people adjust insufficiently from that anchor point to arrive at a final answer. Decision makers are biased towards an initially presented value.

Axiom
annotated source: Anchoring Bias
annotated property: IAO_0000115
annotated target: A cognitive bias, the influence of a particular reference point or anchor on people’s decisions. Often more fully referred to as anchoring-and-adjustment, or anchoring-and-adjusting: after an anchor is set, people adjust insufficiently from that anchor point to arrive at a final answer. Decision makers are biased towards an initially presented value.
has db xref: https://doi.org/10.6028/NIST.SP.1270

Annotator Reporting Bias
type: Class
subclass of: Individual Bias
IAO_0000115: When users rely on automation as a heuristic replacement for their own information seeking and processing. A form of individual bias but often discussed as a group bias, or the larger effects on natural language processing models.

Axiom
annotated source: Annotator Reporting Bias
annotated property: IAO_0000115
annotated target: When users rely on automation as a heuristic replacement for their own information seeking and processing. A form of individual bias but often discussed as a group bias, or the larger effects on natural language processing models.
has db xref: https://doi.org/10.6028/NIST.SP.1270

Automation Complacency Bias
type: Class
subclass of: Individual Bias
IAO_0000115: When humans over-rely on automated systems or have their skills attenuated by such over-reliance (e.g., spelling and autocorrect or spellcheckers).
has exact synonym: Automation Complaceny

Axiom
annotated source: Automation Complacency Bias
annotated property: IAO_0000115
annotated target: When humans over-rely on automated systems or have their skills attenuated by such over-reliance (e.g., spelling and autocorrect or spellcheckers).
has db xref: https://doi.org/10.6028/NIST.SP.1270

Availability Heuristic Bias
type: Class
subclass of: Individual Bias
IAO_0000115: A mental shortcut whereby people tend to overweight what comes easily or quickly to mind, meaning that what is easier to recall—e.g., more “available”—receives greater emphasis in judgement and decision-making.
has exact synonym: Availability Bias
has exact synonym: Availability Heuristic

Axiom
annotated source: Availability Heuristic Bias
annotated property: IAO_0000115
annotated target: A mental shortcut whereby people tend to overweight what comes easily or quickly to mind, meaning that what is easier to recall—e.g., more “available”—receives greater emphasis in judgement and decision-making.
has db xref: https://doi.org/10.6028/NIST.SP.1270

Behavioral Bias
type: Class
subclass of: Individual Bias
IAO_0000115: Systematic distortions in user behavior across platforms or contexts, or across users represented in different datasets.

Axiom
annotated source: Behavioral Bias
annotated property: IAO_0000115
annotated target: Systematic distortions in user behavior across platforms or contexts, or across users represented in different datasets.
has db xref: https://doi.org/10.6028/NIST.SP.1270

Bias
type: Class

Cognitive Bias
type: Class
subclass of: Individual Bias
IAO_0000115: A broad term referring generally to a systematic pattern of deviation from rational judgement and decision-making. A large variety of cognitive biases have been identified over many decades of research in judgement and decision-making, some of which are adaptive mental shortcuts known as heuristics.

Axiom
annotated source: Cognitive Bias
annotated property: IAO_0000115
annotated target: A broad term referring generally to a systematic pattern of deviation from rational judgement and decision-making. A large variety of cognitive biases have been identified over many decades of research in judgement and decision-making, some of which are adaptive mental shortcuts known as heuristics.
has db xref: https://doi.org/10.6028/NIST.SP.1270

Computational Bias
type: Class
subclass of: Bias
IAO_0000115: A systematic tendency which causes differences between results and facts. The bias exists in numbers of the process of data analysis, including the source of the data, the estimator chosen, and the ways the data was analyzed.
has exact synonym: Statistical Bias

Axiom
annotated source: Computational Bias
annotated property: IAO_0000115
annotated target: A systematic tendency which causes differences between results and facts. The bias exists in numbers of the process of data analysis, including the source of the data, the estimator chosen, and the ways the data was analyzed.
has db xref: https://en.wikipedia.org/wiki/Bias_(statistics)

Concept Drift Bias
type: Class
subclass of: Use And Interpretation Bias
IAO_0000115: Use of a system outside the planned domain of application, and a common cause of performance gaps between laboratory settings and the real world.
has exact synonym: Concept Drift

Axiom
annotated source: Concept Drift Bias
annotated property: IAO_0000115
annotated target: Use of a system outside the planned domain of application, and a common cause of performance gaps between laboratory settings and the real world.
has db xref: https://doi.org/10.6028/NIST.SP.1270

Confirmation Bias
type: Class
subclass of: Individual Bias
IAO_0000115: A cognitive bias where people tend to prefer information that aligns with, or confirms, their existing beliefs. People can exhibit confirmation bias in the search for, interpretation of, and recall of information. In the famous Wason selection task experiments, participants repeatedly showed a preference for confirmation over falsification. They were tasked with identifying an underlying rule that applied to number triples they were shown, and they overwhelmingly tested triples that confirmed rather than falsified their hypothesized rule.

Axiom
annotated source: Confirmation Bias
annotated property: IAO_0000115
annotated target: A cognitive bias where people tend to prefer information that aligns with, or confirms, their existing beliefs. People can exhibit confirmation bias in the search for, interpretation of, and recall of information. In the famous Wason selection task experiments, participants repeatedly showed a preference for confirmation over falsification. They were tasked with identifying an underlying rule that applied to number triples they were shown, and they overwhelmingly tested triples that confirmed rather than falsified their hypothesized rule.
has db xref: https://doi.org/10.6028/NIST.SP.1270

Consumer Bias
type: Class
subclass of: Individual Bias
IAO_0000115: Arises when an algorithm or platform provides users with a new venue within which to express their biases, and may occur from either side, or party, in a digital interaction..

Axiom
annotated source: Consumer Bias
annotated property: IAO_0000115
annotated target: Arises when an algorithm or platform provides users with a new venue within which to express their biases, and may occur from either side, or party, in a digital interaction..
has db xref: https://doi.org/10.6028/NIST.SP.1270

Content Production Bias
type: Class
subclass of: Use And Interpretation Bias
IAO_0000115: Arises from structural, lexical, semantic, and syntactic differences in the contents generated by users.

Axiom
annotated source: Content Production Bias
annotated property: IAO_0000115
annotated target: Arises from structural, lexical, semantic, and syntactic differences in the contents generated by users.
has db xref: https://doi.org/10.6028/NIST.SP.1270

Data Dredging Bias
type: Class
subclass of: Use And Interpretation Bias
IAO_0000115: A statistical bias in which testing huge numbers of hypotheses of a dataset may appear to yield statistical significance even when the results are statistically nonsignificant.
has exact synonym: Data Dredging

Axiom
annotated source: Data Dredging Bias
annotated property: IAO_0000115
annotated target: A statistical bias in which testing huge numbers of hypotheses of a dataset may appear to yield statistical significance even when the results are statistically nonsignificant.
has db xref: https://doi.org/10.6028/NIST.SP.1270

Data Generation Bias
type: Class
subclass of: Selection And Sampling Bias
IAO_0000115: Arises from the addition of synthetic or redundant data samples to a dataset.

Axiom
annotated source: Data Generation Bias
annotated property: IAO_0000115
annotated target: Arises from the addition of synthetic or redundant data samples to a dataset.
has db xref: https://doi.org/10.6028/NIST.SP.1270

Deployment Bias
type: Class
subclass of: Group Bias
IAO_0000115: Arises when systems are used as decision aids for humans, since the human intermediary may act on predictions in ways that are typically not modeled in the system. However, it is still individuals using the deployed system.

Axiom
annotated source: Deployment Bias
annotated property: IAO_0000115
annotated target: Arises when systems are used as decision aids for humans, since the human intermediary may act on predictions in ways that are typically not modeled in the system. However, it is still individuals using the deployed system.
has db xref: https://doi.org/10.6028/NIST.SP.1270

Detection Bias
type: Class
subclass of: Selection And Sampling Bias
IAO_0000115: Systematic differences between groups in how outcomes are determined and may cause an over- or underestimation of the size of the effect.

Axiom
annotated source: Detection Bias
annotated property: IAO_0000115
annotated target: Systematic differences between groups in how outcomes are determined and may cause an over- or underestimation of the size of the effect.
has db xref: https://doi.org/10.6028/NIST.SP.1270

Dunning-Kruger Effect Bias
type: Class
subclass of: Cognitive Bias
IAO_0000115: The tendency of people with low ability in a given area or task to overestimate their self-assessed ability. Typically measured by comparing self-assessment with objective performance, often called subjective ability and objective ability, respectively.
has exact synonym: Dunning-Kruger Effect

Axiom
annotated source: Dunning-Kruger Effect Bias
annotated property: IAO_0000115
annotated target: The tendency of people with low ability in a given area or task to overestimate their self-assessed ability. Typically measured by comparing self-assessment with objective performance, often called subjective ability and objective ability, respectively.
has db xref: https://doi.org/10.6028/NIST.SP.1270

Ecological Fallacy Bias
type: Class
subclass of: Selection And Sampling Bias
IAO_0000115: Occurs when an inference is made about an individual based on their membership within a group.
has exact synonym: Ecological Fallacy

Axiom
annotated source: Ecological Fallacy Bias
annotated property: IAO_0000115
annotated target: Occurs when an inference is made about an individual based on their membership within a group.
has db xref: https://doi.org/10.6028/NIST.SP.1270

Emergent Bias
type: Class
subclass of: Use And Interpretation Bias
subclass of: Restriction
  on property: associatedTo
  some values from: UserInterface
IAO_0000115: Emergent bias is the result of the use and reliance on algorithms across new or unanticipated contexts.

Axiom
annotated source: Emergent Bias
annotated property: IAO_0000115
annotated target: Emergent bias is the result of the use and reliance on algorithms across new or unanticipated contexts.
has db xref: https://doi.org/10.6028/NIST.SP.1270

Error Propagation Bias
type: Class
subclass of: Processing Bias
IAO_0000115: The effect of variables' uncertainties (or errors, more specifically random errors) on the uncertainty of a function based on them.
has exact synonym: Error Propagation

Axiom
annotated source: Error Propagation Bias
annotated property: IAO_0000115
annotated target: The effect of variables' uncertainties (or errors, more specifically random errors) on the uncertainty of a function based on them.
has db xref: https://doi.org/10.6028/NIST.SP.1270

Evaluation Bias
type: Class
subclass of: Selection And Sampling Bias
IAO_0000115: Arises when the testing or external benchmark populations do not equally represent the various parts of the user population or from the use of performance metrics that are not appropriate for the way in which the model will be used.

Axiom
annotated source: Evaluation Bias
annotated property: IAO_0000115
annotated target: Arises when the testing or external benchmark populations do not equally represent the various parts of the user population or from the use of performance metrics that are not appropriate for the way in which the model will be used.
has db xref: https://doi.org/10.6028/NIST.SP.1270

Exclusion Bias
type: Class
subclass of: Selection And Sampling Bias
IAO_0000115: When specific groups of user populations are excluded from testing and subsequent analyses.

Axiom
annotated source: Exclusion Bias
annotated property: IAO_0000115
annotated target: When specific groups of user populations are excluded from testing and subsequent analyses.
has db xref: https://doi.org/10.6028/NIST.SP.1270

Feedback Loop Bias
type: Class
subclass of: Use And Interpretation Bias
IAO_0000115: Effects that may occur when an algorithm learns from user behavior and feeds that behavior back into the model.

Axiom
annotated source: Feedback Loop Bias
annotated property: IAO_0000115
annotated target: Effects that may occur when an algorithm learns from user behavior and feeds that behavior back into the model.
has db xref: https://doi.org/10.6028/NIST.SP.1270

Funding Bias
type: Class
subclass of: Group Bias
IAO_0000115: Arises when biased results are reported in order to support or satisfy the funding agency or financial supporter of the research study, but it can also be the individual researcher.

Axiom
annotated source: Funding Bias
annotated property: IAO_0000115
annotated target: Arises when biased results are reported in order to support or satisfy the funding agency or financial supporter of the research study, but it can also be the individual researcher.
has db xref: https://doi.org/10.6028/NIST.SP.1270

Group Bias
type: Class
subclass of: Human Bias
IAO_0000115: A pattern of favoring members of one's in-group over out-group members. This can be expressed in evaluation of others, in allocation of resources, and in many other ways.
has exact synonym: In-group Favoritism
has exact synonym: In-group bias
has exact synonym: In-group preference
has exact synonym: In-group–out-group Bias
has exact synonym: Intergroup bias

Axiom
annotated source: Group Bias
annotated property: IAO_0000115
annotated target: A pattern of favoring members of one's in-group over out-group members. This can be expressed in evaluation of others, in allocation of resources, and in many other ways.
has db xref: https://en.wikipedia.org/wiki/In-group_favoritism

Groupthink Bias
type: Class
subclass of: Group Bias
IAO_0000115: A psychological phenomenon that occurs when people in a group tend to make non-optimal decisions based on their desire to conform to the group, or fear of dissenting with the group. In groupthink, individuals often refrain from expressing their personal disagreement with the group, hesitating to voice opinions that do not align with the group.
has exact synonym: Groupthink

Axiom
annotated source: Groupthink Bias
annotated property: IAO_0000115
annotated target: A psychological phenomenon that occurs when people in a group tend to make non-optimal decisions based on their desire to conform to the group, or fear of dissenting with the group. In groupthink, individuals often refrain from expressing their personal disagreement with the group, hesitating to voice opinions that do not align with the group.
has db xref: https://doi.org/10.6028/NIST.SP.1270

Historical Bias
type: Class
subclass of: Systemic Bias
IAO_0000115: Referring to the long-standing biases encoded in society over time. Related to, but distinct from, biases in historical description, or the interpretation, analysis, and explanation of history. A common example of historical bias is the tendency to view the larger world from a Western or European view.

Axiom
annotated source: Historical Bias
annotated property: IAO_0000115
annotated target: Referring to the long-standing biases encoded in society over time. Related to, but distinct from, biases in historical description, or the interpretation, analysis, and explanation of history. A common example of historical bias is the tendency to view the larger world from a Western or European view.
has db xref: https://doi.org/10.6028/NIST.SP.1270

Hostile Attribution Bias
type: Class
subclass of: Use And Interpretation Bias
IAO_0000115: A bias wherein individuals perceive benign or ambiguous behaviors as hostile.

Axiom
annotated source: Hostile Attribution Bias
annotated property: IAO_0000115
annotated target: A bias wherein individuals perceive benign or ambiguous behaviors as hostile.
has db xref: https://en.wikipedia.org/wiki/Interpretive_bias

Human Bias
type: Class
subclass of: Bias
IAO_0000115: Systematic errors in human thought based on a limited number of heuristic principles and predicting values to simpler judgmental operations.

Axiom
annotated source: Human Bias
annotated property: IAO_0000115
annotated target: Systematic errors in human thought based on a limited number of heuristic principles and predicting values to simpler judgmental operations.
has db xref: https://doi.org/10.6028/NIST.SP.1270

Human Reporting Bias
type: Class
subclass of: Individual Bias
IAO_0000115: When users rely on automation as a heuristic replacement for their own information seeking and processing.

Axiom
annotated source: Human Reporting Bias
annotated property: IAO_0000115
annotated target: When users rely on automation as a heuristic replacement for their own information seeking and processing.
has db xref: https://doi.org/10.6028/NIST.SP.1270

Implicit Bias
type: Class
subclass of: Individual Bias
IAO_0000115: An unconscious belief, attitude, feeling, association, or stereotype that can affect the way in which humans process information, make decisions, and take actions.
has exact synonym: Confirmatory Bias

Axiom
annotated source: Implicit Bias
annotated property: IAO_0000115
annotated target: An unconscious belief, attitude, feeling, association, or stereotype that can affect the way in which humans process information, make decisions, and take actions.
has db xref: https://doi.org/10.6028/NIST.SP.1270

Individual Bias
type: Class
subclass of: Human Bias
IAO_0000115: Individual bias is a persistent point of view or limited list of such points of view that one applies ("parent", "academic", "professional", or etc.).

Axiom
annotated source: Individual Bias
annotated property: IAO_0000115
annotated target: Individual bias is a persistent point of view or limited list of such points of view that one applies ("parent", "academic", "professional", or etc.).
has db xref: https://develop.consumerium.org/wiki/Individual_bias

Inherited Bias
type: Class
subclass of: Processing Bias
IAO_0000115: Arises when applications that are built with machine Learning are used to generate inputs for other machine Learning algorithms. If the output is biased in any way, this bias may be inherited by systems using the output as input to learn other models.

Axiom
annotated source: Inherited Bias
annotated property: IAO_0000115
annotated target: Arises when applications that are built with machine Learning are used to generate inputs for other machine Learning algorithms. If the output is biased in any way, this bias may be inherited by systems using the output as input to learn other models.
has db xref: https://doi.org/10.6028/NIST.SP.1270

Institutional Bias
type: Class
subclass of: Systemic Bias
IAO_0000115: In contrast to biases exhibited at the level of individual persons, institutional bias refers to a tendency exhibited at the level of entire institutions, where practices or norms result in the favoring or disadvantaging of certain social groups. Common examples include institutional racism and institutional sexism.

Axiom
annotated source: Institutional Bias
annotated property: IAO_0000115
annotated target: In contrast to biases exhibited at the level of individual persons, institutional bias refers to a tendency exhibited at the level of entire institutions, where practices or norms result in the favoring or disadvantaging of certain social groups. Common examples include institutional racism and institutional sexism.
has db xref: https://doi.org/10.6028/NIST.SP.1270

Interpretation Bias
type: Class
subclass of: Individual Bias
IAO_0000115: A form of information processing bias that can occur when users interpret algorithmic outputs according to their internalized biases and views.

Axiom
annotated source: Interpretation Bias
annotated property: IAO_0000115
annotated target: A form of information processing bias that can occur when users interpret algorithmic outputs according to their internalized biases and views.
has db xref: https://doi.org/10.6028/NIST.SP.1270

Linking Bias
type: Class
subclass of: Use And Interpretation Bias
IAO_0000115: Arises when network attributes obtained from user connections, activities, or interactions differ and misrepresent the true behavior of the users.

Axiom
annotated source: Linking Bias
annotated property: IAO_0000115
annotated target: Arises when network attributes obtained from user connections, activities, or interactions differ and misrepresent the true behavior of the users.
has db xref: https://doi.org/10.6028/NIST.SP.1270

Loss Of Situational Awareness Bias
type: Class
subclass of: Individual Bias
IAO_0000115: When automation leads to humans being unaware of their situation such that, when control of a system is given back to them in a situation where humans and machines cooperate, they are unprepared to assume their duties. This can be a loss of awareness over what automation is and isn’t taking care of.

Axiom
annotated source: Loss Of Situational Awareness Bias
annotated property: IAO_0000115
annotated target: When automation leads to humans being unaware of their situation such that, when control of a system is given back to them in a situation where humans and machines cooperate, they are unprepared to assume their duties. This can be a loss of awareness over what automation is and isn’t taking care of.
has db xref: https://doi.org/10.6028/NIST.SP.1270

Measurement Bias
type: Class
subclass of: Selection And Sampling Bias
IAO_0000115: Arises when features and labels are proxies for desired quantities, potentially leaving out important factors or introducing group or input-dependent noise that leads to differential performance.

Axiom
annotated source: Measurement Bias
annotated property: IAO_0000115
annotated target: Arises when features and labels are proxies for desired quantities, potentially leaving out important factors or introducing group or input-dependent noise that leads to differential performance.
has db xref: https://doi.org/10.6028/NIST.SP.1270

Mode Confusion Bias
type: Class
subclass of: Individual Bias
IAO_0000115: When modal interfaces confuse human operators, who misunderstand which mode the system is using, taking actions which are correct for a different mode but incorrect for their current situation. This is the cause of many deadly accidents, but also a source of confusion in everyday life.

Axiom
annotated source: Mode Confusion Bias
annotated property: IAO_0000115
annotated target: When modal interfaces confuse human operators, who misunderstand which mode the system is using, taking actions which are correct for a different mode but incorrect for their current situation. This is the cause of many deadly accidents, but also a source of confusion in everyday life.
has db xref: https://doi.org/10.6028/NIST.SP.1270

Model Selection Bias
type: Class
subclass of: Processing Bias
IAO_0000115: The bias introduced while using the data to select a single seemingly “best” model from a large set of models employing many predictor variables. Model selection bias also occurs when an explanatory variable has a weak relationship with the response variable.

Axiom
annotated source: Model Selection Bias
annotated property: IAO_0000115
annotated target: The bias introduced while using the data to select a single seemingly “best” model from a large set of models employing many predictor variables. Model selection bias also occurs when an explanatory variable has a weak relationship with the response variable.
has db xref: https://doi.org/10.6028/NIST.SP.1270

Popularity Bias
type: Class
subclass of: Selection And Sampling Bias
subclass of: Restriction
  on property: associatedTo
  some values from: RecommenderSystem
subclass of: Restriction
  on property: associatedTo
  some values from: SearchEngine
IAO_0000115: A form of selection bias that occurs when items that are more popular are more exposed and less popular items are under-represented.
comment: When collaborative filtering recommenders typically emphasize popular items (those with more ratings) over other “long-tail”, less popular ones that may only be popular among small groups of users.

Axiom
annotated source: Popularity Bias
annotated property: IAO_0000115
annotated target: A form of selection bias that occurs when items that are more popular are more exposed and less popular items are under-represented.
has db xref: https://doi.org/10.6028/NIST.SP.1270

Population Bias
type: Class
subclass of: Selection And Sampling Bias
IAO_0000115: A form of selection bias that occurs when items that are more popular are more exposed and less popular items are under-represented. Systematic distortions in demographics or other user characteristics between a population of users represented in a dataset or on a platform and some target population.
comment: Population bias arises when statistics, demographics, representatives, and user characteristics are different in the user population of the platform from the original target population

Axiom
annotated source: Population Bias
annotated property: IAO_0000115
annotated target: A form of selection bias that occurs when items that are more popular are more exposed and less popular items are under-represented. Systematic distortions in demographics or other user characteristics between a population of users represented in a dataset or on a platform and some target population.
has db xref: https://doi.org/10.6028/NIST.SP.1270

Presentation Bias
type: Class
subclass of: Individual Bias
IAO_0000115: Biases arising from how information is presented on the Web, via a user interface, due to rating or ranking of output, or through users’ own self-selected, biased interaction.

Axiom
annotated source: Presentation Bias
annotated property: IAO_0000115
annotated target: Biases arising from how information is presented on the Web, via a user interface, due to rating or ranking of output, or through users’ own self-selected, biased interaction.
has db xref: https://doi.org/10.6028/NIST.SP.1270

Processing Bias
type: Class
subclass of: Computational Bias
IAO_0000115: Judgement modulated by affect, which is influenced by the level of efficacy and efficiency in information processing; in cognitive sciences, processing bias is often referred to as an aesthetic judgement.
has exact synonym: Validation Bias

Axiom
annotated source: Processing Bias
annotated property: IAO_0000115
annotated target: Judgement modulated by affect, which is influenced by the level of efficacy and efficiency in information processing; in cognitive sciences, processing bias is often referred to as an aesthetic judgement.
has db xref: https://royalsocietypublishing.org/doi/10.1098/rspb.2019.0165#d1e5237

Ranking Bias
type: Class
subclass of: Anchoring Bias
IAO_0000115: The idea that top-ranked results are the most relevant and important and will result in more clicks than other results.

Axiom
annotated source: Ranking Bias
annotated property: IAO_0000115
annotated target: The idea that top-ranked results are the most relevant and important and will result in more clicks than other results.
has db xref: https://doi.org/10.6028/NIST.SP.1270

Rashomon Effect Bias
type: Class
subclass of: Individual Bias
IAO_0000115: Refers to differences in perspective, memory and recall, interpretation, and reporting on the same event from multiple persons or witnesses.
has exact synonym: Rashomon Effect
has exact synonym: Rashomon Principle

Axiom
annotated source: Rashomon Effect Bias
annotated property: IAO_0000115
annotated target: Refers to differences in perspective, memory and recall, interpretation, and reporting on the same event from multiple persons or witnesses.
has db xref: https://doi.org/10.6028/NIST.SP.1270

Representation Bias
type: Class
subclass of: Selection And Sampling Bias
IAO_0000115: Arises due to non-random sampling of subgroups, causing trends estimated for one population to not be generalizable to data collected from a new population.

Axiom
annotated source: Representation Bias
annotated property: IAO_0000115
annotated target: Arises due to non-random sampling of subgroups, causing trends estimated for one population to not be generalizable to data collected from a new population.
has db xref: https://doi.org/10.6028/NIST.SP.1270

Selection And Sampling Bias
type: Class
subclass of: Computational Bias
IAO_0000115: Bias introduced by the selection of individuals, groups, or data for analysis in such a way that proper randomization is not achieved, thereby failing to ensure that the sample obtained is representative of the population intended to be analyzed.
has exact synonym: Sampling Bias
has exact synonym: Selection Bias
has exact synonym: Selection Effect
comment: When the sampling method only reaches a portion of the population or the population of interest has changed or is distinct from the population used during model training. https://doi.org/10.1145/3588433

Axiom
annotated source: Selection And Sampling Bias
annotated property: IAO_0000115
annotated target: Bias introduced by the selection of individuals, groups, or data for analysis in such a way that proper randomization is not achieved, thereby failing to ensure that the sample obtained is representative of the population intended to be analyzed.
has db xref: https://en.wikipedia.org/wiki/Selection_bias

Selective Adherence Bias
type: Class
subclass of: Individual Bias
IAO_0000115: Decision-makers’ inclination to selectively adopt algorithmic advice when it matches their pre-existing beliefs and stereotypes.

Axiom
annotated source: Selective Adherence Bias
annotated property: IAO_0000115
annotated target: Decision-makers’ inclination to selectively adopt algorithmic advice when it matches their pre-existing beliefs and stereotypes.
has db xref: https://doi.org/10.6028/NIST.SP.1270

Societal Bias
type: Class
subclass of: Systemic Bias
IAO_0000115: Can be positive or negative, and take a number of different forms, but is typically characterized as being for or against groups or individuals based on social identities, demographic factors, or immutable physical characteristics. Societal or social biases are often stereotypes. Common examples of societal or social biases are based on concepts like race, ethnicity, gender, sexual orientation, socioeconomic status, education, and more. Societal bias is often recognized and discussed in the context of NLP (Natural Language Processing) models.
has exact synonym: Social Bias

Axiom
annotated source: Societal Bias
annotated property: IAO_0000115
annotated target: Can be positive or negative, and take a number of different forms, but is typically characterized as being for or against groups or individuals based on social identities, demographic factors, or immutable physical characteristics. Societal or social biases are often stereotypes. Common examples of societal or social biases are based on concepts like race, ethnicity, gender, sexual orientation, socioeconomic status, education, and more. Societal bias is often recognized and discussed in the context of NLP (Natural Language Processing) models.
has db xref: https://doi.org/10.6028/NIST.SP.1270

Streetlight Effect Bias
type: Class
subclass of: Individual Bias
IAO_0000115: A bias whereby people tend to search only where it is easiest to look.
has exact synonym: Streetlight Effect

Axiom
annotated source: Streetlight Effect Bias
annotated property: IAO_0000115
annotated target: A bias whereby people tend to search only where it is easiest to look.
has db xref: https://doi.org/10.6028/NIST.SP.1270

Sunk Cost Fallacy Bias
type: Class
subclass of: Group Bias
IAO_0000115: A human tendency where people opt to continue with an endeavor or behavior due to previously spent or invested resources, such as money, time, and effort, regardless of whether costs outweigh benefits. For example, in AI, the sunk cost fallacy could lead development teams and organizations to feel that because they have already invested so much time and money into a particular AI application, they must pursue it to market rather than deciding to end the effort, even in the face of significant technical debt and/or ethical debt.
has exact synonym: Sunk Cost Fallacy

Axiom
annotated source: Sunk Cost Fallacy Bias
annotated property: IAO_0000115
annotated target: A human tendency where people opt to continue with an endeavor or behavior due to previously spent or invested resources, such as money, time, and effort, regardless of whether costs outweigh benefits. For example, in AI, the sunk cost fallacy could lead development teams and organizations to feel that because they have already invested so much time and money into a particular AI application, they must pursue it to market rather than deciding to end the effort, even in the face of significant technical debt and/or ethical debt.
has db xref: https://doi.org/10.6028/NIST.SP.1270

Survivorship Bias
type: Class
subclass of: Processing Bias
IAO_0000115: Tendency for people to focus on the items, observations, or people that “survive” or make it past a selection process, while overlooking those that did not.

Axiom
annotated source: Survivorship Bias
annotated property: IAO_0000115
annotated target: Tendency for people to focus on the items, observations, or people that “survive” or make it past a selection process, while overlooking those that did not.
has db xref: https://doi.org/10.6028/NIST.SP.1270

Systemic Bias
type: Class
subclass of: Bias
IAO_0000115: Biases that result from procedures and practices of particular institutions that operate in ways which result in certain social groups being advantaged or favored and others being disadvantaged or devalued.
has exact synonym: Institutional Bias
has exact synonym: Societal Bias

Axiom
annotated source: Systemic Bias
annotated property: IAO_0000115
annotated target: Biases that result from procedures and practices of particular institutions that operate in ways which result in certain social groups being advantaged or favored and others being disadvantaged or devalued.
has db xref: https://doi.org/10.6028/NIST.SP.1270

Temporal Bias
type: Class
subclass of: Selection And Sampling Bias
IAO_0000115: Bias that arises from differences in populations and behaviors over time.

Axiom
annotated source: Temporal Bias
annotated property: IAO_0000115
annotated target: Bias that arises from differences in populations and behaviors over time.
has db xref: https://doi.org/10.6028/NIST.SP.1270

Uncertainty Bias
type: Class
subclass of: Selection And Sampling Bias
IAO_0000115: Arises when predictive algorithms favor groups that are better represented in the training data, since there will be less uncertainty associated with those predictions.

Axiom
annotated source: Uncertainty Bias
annotated property: IAO_0000115
annotated target: Arises when predictive algorithms favor groups that are better represented in the training data, since there will be less uncertainty associated with those predictions.
has db xref: https://doi.org/10.6028/NIST.SP.1270

Use And Interpretation Bias
type: Class
subclass of: Computational Bias
IAO_0000115: An information-processing bias, the tendency to inappropriately analyze ambiguous stimuli, scenarios and events.
has exact synonym: Interpretive Bias

Axiom
annotated source: Use And Interpretation Bias
annotated property: IAO_0000115
annotated target: An information-processing bias, the tendency to inappropriately analyze ambiguous stimuli, scenarios and events.
has db xref: https://en.wikipedia.org/wiki/Interpretive_bias

User Interaction Bias
type: Class
subclass of: Individual Bias
IAO_0000115: Arises when a user imposes their own self-selected biases and behavior during interaction with data, output, results, etc.

Axiom
annotated source: User Interaction Bias
annotated property: IAO_0000115
annotated target: Arises when a user imposes their own self-selected biases and behavior during interaction with data, output, results, etc.
has db xref: https://doi.org/10.6028/NIST.SP.1270

Simpon's Paradox Bias
type: Class
subclass of: Ecological Fallacy Bias
IAO_0000115: A statistical phenomenon where the marginal association between two categorical variables is qualitatively different from the partial association between the same two variables after controlling for one or more other variables. For example, the statistical association or correlation that has been detected between two variables for an entire population disappears or reverses when the population is divided into subgroups.
has exact synonym: Simpson's Paradox

Axiom
annotated source: Simpon's Paradox Bias
annotated property: IAO_0000115
annotated target: A statistical phenomenon where the marginal association between two categorical variables is qualitatively different from the partial association between the same two variables after controlling for one or more other variables. For example, the statistical association or correlation that has been detected between two variables for an entire population disappears or reverses when the population is divided into subgroups.
has db xref: https://doi.org/10.6028/NIST.SP.1270

ModelComponent
type: Class
subclass of: Thing
comment: Model Components are the components of predictive model in use. Different types of predictive model have different components. For example, a Bayesian Network Model will have components like Bayesian Network Nodes, a Rule Set Model will have Rule Set as components and Neural Network Model will have Neural Layer, Neural Inputs/Outputs as modelling components.

Output
type: Class
subclass of: Thing
comment: Output is the output of classification model.

CausalRiskDifference
type: NamedIndividual
type: Socio-Demographic Bias Measure
comment: They define p2c to be: sum_of s all values S, dec (s) = theta w(s) / sum_of s all values S w(s) RDc not close to zero means that there is a bias in decision value due to group membership (causal discrimination) or to covariates that have not been accounted for in the analysis (omitted variable bias).
is defined by: Causal Risk Difference
formalization: RDc = p1 − p2c

Data Coverage Measure
type: NamedIndividual
type: Representation Bias Measure
definition: To have enough similar entries (sub-categories) for a particular variable (categoy) in a dataset.
formalization: cov(q,D) = |G_D(q)|, threshold value k, qu is covered iff cov (q, D) > k.

Differential Bias Embeddings
type: NamedIndividual
type: Socio-Demographic Bias Measure
comment: A preprocessing method for the discovery of the problematic documents in the training corpus that have biases in them and tried to debias the system by perturbing or removing these documents efficiently from the training corpus.
definition: Differential Bias Embeddings
definition: Given a word embedding, this method reveals how perturbing the training corpus would affect the resulting embedding bias.
formalization: ∆pB = B(w) − B( ˜w)

Direct Bias Embeddings
type: NamedIndividual
type: Gender Bias Measure
type: Restriction
  on property: measures
  some values from: Gender Bias
definition: A method to debias word embeddings by proposing a method that respects the embeddings for gender-specific words but debiases embeddings for gender-neutral words.
formalization: 1 / |N | sum_of w2N |cos( ~>w, g)|c

ELMo Bias Embeddings
type: NamedIndividual
type: Gender Bias Measure
definition: A new intrinsic analysis of how ELMo represents gender in biased ways.

Error Type-1 Unweighted
type: NamedIndividual
type: Gender Bias Measure
definition: This is a type of error that measures names that are tagged as non-PERSON, or not tagged at all. In other words, any name not tagged as a PERSON is considered to be an error
formalization: ∑ n∈Nf I(ntype ≠ PERSON ) / |Nf |

Error Type-1 Weighted
type: NamedIndividual
type: Gender Bias Measure
comment: Where f reqf (·) returns the frequency of a name in the female census data in a particular year. Similarly, f reqm(·) will yield the frequency of a name in the male census data. Type-1 errors can be sub-divided into Type-2 and Type-3 errors and serve as a super-set for the following types.
definition: This type of error is similar to Error Type-1 Unweighted; however, we considered how frequent the mistaken name is based on census data while calculating the error.
formalization: ∑ n∈Nf f reqf (ntype ≠ PERSON ) / ∑ n∈Nf f reqf (n)

Error Type-2 Unweighted
type: NamedIndividual
type: Gender Bias Measure
comment: where ∅ indicates the name is not tagged.
definition: This is a type of error in which only names that are tagged, but whose tags are non-PERSON are considered to be errors. This measure is similar to precision, where the model is only punished for incorrect retrieval. This error rate reports the percentage of names that are tagged as non-PERSON entities among all the names in a certain year.
formalization: ∑ n∈Nf I(ntype /∈ {∅, PERSON }) , |Nf |

Error Type-2 Weighted
type: NamedIndividual
type: Gender Bias Measure
definition: This type of error is similar to Error Type-2 Unweighted; however, here we considered how frequent the mistaken name was while calculating the error.
formalization: ∑ n∈Nf f reqf (ntype /∈ {∅, PERSON }), ∑ n∈Nf f reqf (n)

Error Type-3 Unweighted
type: NamedIndividual
type: Gender Bias Measure
definition: This is a type of error in which only names that are not tagged are considered to be errors. We do not consider names that are wrongfully tagged to non-PERSON as an error, but only names that are not tagged are considered erroneous. This error rate reports the percentage of names that are not tagged at all among all the names in a certain year.
formalization: ∑ n∈Nf I(ntype = ∅), |Nf |

Error Type-3 Weighted
type: NamedIndividual
type: Gender Bias Measure
definition: This type of error is similar to Error Type-3 Unweighted; however, we considered how frequent the mistaken name was while calculating the error.
formalization: ∑ n∈Nf f reqf (ntype = ∅) , ∑n∈Nf f reqf (n)

Gender Bias Score
type: NamedIndividual
type: Gender Bias Measure
type: Restriction
  on property: measures
  some values from: Gender Bias
comment: A metric for measuring gender bias in a generated text from a language model based on recurrent neural networks that is trained on a text corpus along with measuring the bias in the training text itself.
comment: w is any word in the corpus, f is a set of gendered words ("female"), m is a sit of gendered words ("male"). The bias measure uses the mean absolute and the standard deviation.
formalization: bias(w) = log ( P(w|f) / P(w|m) )

Gender Bias vSRL
type: NamedIndividual
type: Gender Bias Measure
type: Restriction
  on property: measures
  some values from: Gender Bias
note: The goal is to identify the correlations that are potentially amplified by a learned model. To achieve this, a bias score is defined for a given output, o, with respect to a demographic variable, g; where c(o, g) is the number of occurrences of o and g in a corpus.
note: To evaluate the degree of bias amplification, we propose to compare bias scores on the training set, b∗(o, g), with bias scores on an unlabeled evaluation set of images ˜b(o, g) that has been annotated by a predictor. We assume that the evaluation set is identically distributed to the training set. Therefore, if o is positively correlated with g (i.e,b∗(o, g) > 1/‖G‖) and ˜b(o, g) is larger than b∗(o, g), we say bias has been amplified.
formalization: b(o, g) = c(o, g) / ∑ g′∈G c(o, g′) '

Gini coefficient of the in-degree distribution
type: NamedIndividual
type: Popularity Bias Measure
comment: The criteria followed to determine if there is presence of bias is the higher the Gini coefficient, the more skewed or unequal the in-degree distribution across all nodes.
definition: The Gini coefficient allows to demonstrate whether popularity bias is exacerbated by recommendation algorithms regardless of the initial conditions of the network structure, or whether certain types of networks are exempt from this bias. Gini coefficient of the in-degree distribution
formalization: Gini(π^in ) = {sum_of_all (2_i − n − 1)π_i^in} / {sum_of_all π_i^in}

In-group links
type: NamedIndividual
type: Popularity Bias Measure
definition: Measures the fraction of links within groups to determine what type of edges are being recommended more often by the recommendation algorithms.
formalization: I_a = {e_aa} / {e_aa + e_ab}

Learned Latent Structure
type: NamedIndividual
type: UnderlyingDistributionSkewMeasure
comment: Consider the problem of binary classification in which we are presented with a set of paired training data samples Dt r ain = {(x(i), y(i))}n i=1 consisting of features x ∈ Rm and labels y ∈ Rd. Our goal is to find a functional mapping f : X → Y parameterized by θ which minimizes a certain loss L(θ ) over our entire training dataset.
definition: A tunable algorithm for mitigating the hidden, and potentially unknown, biases within training data. The algorithm fuses the original learning task with a variational autoencoder to learn the latent structure within the dataset.
formalization: θ∗ = arg min_θ 1/ n sum_of Li (θ)

Low-Degree Node Bias
type: NamedIndividual
type: Linking Bias Measure
definition: In the context of community detection, when theses methods ignore lowly connected users that have few links—by putting them into insignificant small or singleton communities which are then ignored in the study.
note: |ci| denotes the size of the community ci, and θ is athreshold value that can be treated as a hyperparameter and will be used in our proposed algorithm. We call node n an insignificantly labeled node if:∃ cinsig such that n ∈ cinsig where cinsig is an insignificant community.
formalization: C_insig = {ci| where |ci| < θ}

Nationality Group Bias
type: NamedIndividual
type: Nationality Bias Measure
is defined by: Nationality Bias in Text Generation https://aclanthology.org/2023.eacl-main.9 Narayanan Venkit et al., EACL 2023
definition: Demographic group bias is measured by using statistical inferences of different demographic synonym groups d_n and check for parity across all the groups and a standard control group C, using the story generation feature of the GPT-2 language model.

RepresentationRate
type: NamedIndividual
type: Representation Bias Measure
definition: An equal base rate is defined as having an equal number of objects for different subgroups in the dataset.

Situation Testing Causal Inference
type: NamedIndividual
type: Socio-Demographic Bias Measure
comment: Given a target tuple t with c = c and e = e , all the tuples are ranked according to their distances to t using a distance function defined on an appropriate subset of nonprotected attributes. Then, the tuples that are within top-2K highest in the ranking for one-to-one pairing are selected. The selected tuples with c+ are added into set S+ , and the selected tuples with c are added into set S . The proportion p 1 of tuples with e+ among the tuples in S+, and the proportion p 2 of tuples with e+ among the tuples in S are compared. The dierence between p 1 and p2 , i.e., p 1 p2 reflects the bias due to the membership to the protected group. If p1 p2 is greater than a threshold tau, then t is considered as being discriminated. The value of threshold tau used for discrimination depends on the law.
definition: A general technique to capture discrimination based on the legally grounded situation testing methodology.

Uncovering Simpson paradox for trends
type: NamedIndividual
type: Simpsons Paradox Measure
comment: The equations hold if the expected value of Y is a monotonically increasing (or decreasing) function of Xp alone, but conditioned on Xc is a monotonically decreasing (resp. increasing) function of Xp , or is constant.
comment: Y is the dependent variable in the dataset, i.e., an outcome being measured, and as X = {X1, X2, . . . , Xm } the set of m independent variables or features. The goal of the method is to identify pairs of variables (Xp , Xc )—Simpson’s pairs— such that a trend in Y as a function of Xp disappears or reverses when the data is disaggregated by conditioning on Xc .
definition: A statistical method to systematically uncover instances of Simpson’s paradox in heterogeneous data.
formalization: dE[Y |Xp = xp ]/dxp < 0, dE[Y |Xp = xp , Xc = xc ]/dxp ≥ 0

Visibility of the minority group
type: NamedIndividual
type: Representation Bias Measure
definition: Change in visibility by computing prediction{f_m} after and before the recommendations to verify whether the minority group is gaining or losing visibility.
formalization: delta function_fm = prediction{f_m (after)} − prediction{f_m (before)}

Sentence Encoder Association Test (SEAT)
type: NamedIndividual

Simpson’s Disaggregation
type: NamedIndividual
type: Simpsons Paradox Measure
comment: The method takes as input a set of observations of an outcome, Y , and a set of covariates, or features, X ={X1, X2, ..., Xm} associated with it. The approach has three steps. First, it disaggregates data into more homogenous subgroups based on some covariate Xc. Next, it uses a linear model to capture trends with respect to some other covariate Xj , both within the subgroups and within the aggregate data. Finally, it quantifies how well the models describe the disaggregated data compared to aggregate data to identify the important disaggregations.
is defined by: Nazanin Alipourfard, Peter G. Fennell, and Kristina Lerman. 2018. Using Simpson’s paradox to discover interesting patterns in behavioral data. In Proceedings of the 12th International AAAI Conference on Web and Social Media.
definition: A method for automatically uncovering interesting patterns in data based on Simpson's Paradox.

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