Explanation Faithfulness Evaluation Measures Ontology - Individuals File
type: Ontology
creator: Danielle Villa: https://tw.rpi.edu/person/danielle-villa
imports: http://www.semanticweb.org/villad4/ontologies/efemo/1.0
version IRI: http://www.semanticweb.org/villad4/ontologies/2025/11/efemo_indv/1.0

Unfaithfulness Explained by Bias Evaluation Method
type: NamedIndividual
type: Evaluation Method
type: Perturbation Based Evaluation Method
type: Robustness Evaluation Method
evaluates proxy: self consistency proxy
is defined by: Turpin, M., Michael, J., Perez, E., & Bowman, S. (2023). Language models don't always say what they think: Unfaithful explanations in chain-of-thought prompting. Advances in Neural Information Processing Systems, 36, 74952-74965.
definition: change in prediction that is stereotype-aligned when the demographic info of the input is changed and the explanation fails to reflect this

Improving the faithfulness of attention-based explanations with task-specific information for text classification
type: NamedIndividual
type: faithfulness document
direct source: Chrysostomou, G., & Aletras, N. (2021). Improving the faithfulness of attention-based explanations with task-specific information for text classification. arXiv preprint arXiv:2105.02657.

Comprehensiveness Evaluation Method
type: NamedIndividual
type: Computational Based Evaluation Method
type: Evaluation Method
type: Perturbation Based Evaluation Method
type: correctness evaluation method
evaluates proxy: polarity consistency proxy
is defined by: DeYoung, J., Jain, S., Rajani, N. F., Lehman, E., Xiong, C., Socher, R., & Wallace, B. C. (2020, July). ERASER: A benchmark to evaluate rationalized NLP models. In Proceedings of the 58th annual meeting of the association for computational linguistics (pp. 4443-4458).
definition: change in prediction probability after removing most important X%/# of tokens

Comprehensiveness Identifier
type: NamedIndividual
type: Faithfulness Measure identifier
has textual name: COMP
has textual name: Comprehensiveness
identifies: Comprehensiveness

Consistency Proxy
type: NamedIndividual
type: Faithfulness Proxy
is defined by: Dasgupta, S., Frost, N., & Moshkovitz, M. (2022, June). Framework for evaluating faithfulness of local explanations. In International Conference on Machine Learning (pp. 4794-4815). PMLR.
definition: A faithfulness proxy that states that if two inputs are given the same explanation, they should be given the same prediction.
direct source: Dasgupta, S., Frost, N., & Moshkovitz, M. (2022, June). Framework for evaluating faithfulness of local explanations. In International Conference on Machine Learning (pp. 4794-4815). PMLR.

Framework for evaluating faithfulness of local explanations
type: NamedIndividual
type: faithfulness document
direct source: Dasgupta, S., Frost, N., & Moshkovitz, M. (2022, June). Framework for evaluating faithfulness of local explanations. In International Conference on Machine Learning (pp. 4794-4815). PMLR.

Decision Flip - Most Informative Token
type: NamedIndividual
type: Faithfulness Measure
description: % decision flips after removing most important token
evaluates modality: attention map explanation modality
has granularity: local scope
has model specificity: model agnostic
measures proxy: polarity consistency proxy
requires access level: inference access level
uses method: DFMIT Evaluation Method
was attributed to: Improving the faithfulness of attention-based explanations with task-specific information for text classification

ERASER: A benchmark to evaluate rationalized NLP models
type: NamedIndividual
type: faithfulness document
direct source: DeYoung, J., Jain, S., Rajani, N. F., Lehman, E., Xiong, C., Socher, R., & Wallace, B. C. (2020, July). ERASER: A benchmark to evaluate rationalized NLP models. In Proceedings of the 58th annual meeting of the association for computational linguistics (pp. 4443-4458).

Decision Flip - Most Important Token Identifier
type: NamedIndividual
type: Faithfulness Measure identifier
has textual name: DFMIT
has textual name: Decision Flip - Most Informative Token
identifies: Decision Flip - Most Informative Token

DFMIT Evaluation Method
type: NamedIndividual
type: Computational Based Evaluation Method
type: Evaluation Method
type: Perturbation Based Evaluation Method
type: correctness evaluation method
evaluates proxy: polarity consistency proxy
is defined by: Chrysostomou, G., & Aletras, N. (2021). Improving the faithfulness of attention-based explanations with task-specific information for text classification. arXiv preprint arXiv:2105.02657.
definition: % decision flips after removing most important token

Comprehensiveness
type: NamedIndividual
type: Faithfulness Measure
description: change in prediction probability after removing most important X%/# of tokens
evaluates modality: attention map explanation modality
has granularity: local scope
has model specificity: model agnostic
measures proxy: polarity consistency proxy
requires access level: inference access level
uses method: Comprehensiveness Evaluation Method
was attributed to: ERASER: A benchmark to evaluate rationalized NLP models

Global Consistency
type: NamedIndividual
type: Faithfulness Measure
description: The global consistency of explanation system (f, e), with respect to distribution µ over X , is m^c = E_{x∈µX}[m^c (x)].
evaluates modality: cot explanation modality
has granularity: global scope
has model specificity: model agnostic
measures proxy: Consistency Proxy
measures proxy: self consistency proxy
requires access level: inference access level
uses method: Global Consistency Evaluation Method
was attributed to: Framework for evaluating faithfulness of local explanations

Global Consistency Identifier
type: NamedIndividual
type: Faithfulness Measure identifier
has textual name: Global Consistency
identifies: Global Consistency

Global Consistency Evaluation Method
type: NamedIndividual
type: Axiomatic Evaluation Method
type: Computational Based Evaluation Method
type: Evaluation Method
evaluates proxy: Consistency Proxy
is defined by: Dasgupta, S., Frost, N., & Moshkovitz, M. (2022, June). Framework for evaluating faithfulness of local explanations. In International Conference on Machine Learning (pp. 4794-4815). PMLR.
definition: the expectation across the entire dataset that two inputs that are assigned the same explanation will receive the same output

Attention is not explanation
type: NamedIndividual
type: faithfulness document
direct source: Jain, S., & Wallace, B. C. (2019). Attention is not explanation. arXiv preprint arXiv:1902.10186.

Local Sufficiency
type: NamedIndividual
type: Faithfulness Measure
description: The local sufficiency of explainer e for model f at instance x, with respect to distribution µ, is defined as m^s(x) = Pr_{x′∈µC_x}(f(x′) = f(x)). Recall that the notation x′ ∈µ Cx means “x′ is drawn from distribution µ restricted to the set C_x”
evaluates modality: cot explanation modality
has granularity: local scope
has model specificity: model agnostic
measures proxy: minimality proxy
requires access level: inference access level
uses method: Local Sufficiency Evaluation Method
was attributed to: Framework for evaluating faithfulness of local explanations

Local Sufficiency Identifier
type: NamedIndividual
type: Faithfulness Measure identifier
has textual name: Local Sufficiency
identifies: Local Sufficiency

Local Sufficiency Evaluation Method
type: NamedIndividual
type: Axiomatic Evaluation Method
type: Computational Based Evaluation Method
type: Evaluation Method
evaluates proxy: minimality proxy
is defined by: Dasgupta, S., Frost, N., & Moshkovitz, M. (2022, June). Framework for evaluating faithfulness of local explanations. In International Conference on Machine Learning (pp. 4794-4815). PMLR.
definition: the probability that two inputs for which the same explanation holds true will have the same output

RandomPermute
type: NamedIndividual
type: Faithfulness Measure
description: change in prediction probability after randomizing all attention weights
evaluates modality: attention map explanation modality
has granularity: local scope
has model specificity: transformer specific
measures proxy: robustness equivalence proxy
requires access level: attention weight access level
requires access level: inference access level
uses method: Random Permute Evaluation Method
was attributed to: Attention is not explanation

Random Permute Evaluation Method
type: NamedIndividual
type: Computational Based Evaluation Method
type: Evaluation Method
type: Perturbation Based Evaluation Method
evaluates proxy: robustness equivalence proxy
is defined by: Jain, S., & Wallace, B. C. (2019). Attention is not explanation. arXiv preprint arXiv:1902.10186.
definition: change in prediction probability after randomizing all attention weights

RandomPermute Identifier
type: NamedIndividual
type: Faithfulness Measure identifier
has textual name: RandomPermute
identifies: RandomPermute

Language models dont always say what they think: unfaithful explanations in chain-of-thought prompting
type: NamedIndividual
type: faithfulness document
direct source: Turpin, M., Michael, J., Perez, E., & Bowman, S. (2023). Language models don't always say what they think: Unfaithful explanations in chain-of-thought prompting. Advances in Neural Information Processing Systems, 36, 74952-74965.

Unfaithfulness Explained by Bias Identifier
type: NamedIndividual
type: Faithfulness Measure identifier
has textual name: Unfaithfulness Explained by Bias
identifies: Unfaithfulness Explained by Bias

Unfaithfulness Explained by Bias
type: NamedIndividual
type: Faithfulness Measure
description: change in prediction that is stereotype-aligned when the demographic info of the input is changed and the explanation fails to reflect this
evaluates modality: attention map explanation modality
evaluates modality: cot explanation modality
has granularity: local scope
has model specificity: model agnostic
measures proxy: self consistency proxy
requires access level: inference access level
uses method: Unfaithfulness Explained by Bias Evaluation Method
was attributed to: Language models dont always say what they think: unfaithful explanations in chain-of-thought prompting