# Write a data ethics case study

## Instructions

You've learned about various [data ethics challenges](../../data-science/introduction/data-science-ethics.md#ethics-challenges) and seen some examples of [case studies](../../data-science/introduction/data-science-ethics.md#case-studies) reflecting data ethics challenges in real-world contexts.

In this assignment, you'll write your own case study reflecting a data ethics challenge from your own experience, or from a relevant real-world context you are familiar with. Just follow these steps:

1. **Pick a data ethics challenge**. Look at [the section examples](../../data-science/introduction/data-science-ethics.md#ethics-challenges) or explore online examples like [the Deon Checklist](https://deon.drivendata.org/examples/) to get inspiration.
2. **Describe a real-world example**. Think about a situation you have heard of (headlines, research study etc.) or experienced (local community), where this specific challenge occurred. Think about the data ethics questions related to the challenge - and discuss the potential harms or unintended consequences that arise because of this issue. Bonus points: think about potential solutions or processes that may be applied here to help eliminate or mitigate the adverse impact of this challenge.
3. **Provide a related resources list**. Share one or more resources (links to an article, a personal blog post or image, an online research paper etc.) to prove this was a real-world occurrence. Bonus points: share resources that also showcase the potential harms & consequences of the incident, or highlight positive steps taken to prevent its recurrence.

## Rubric

Exemplary | Adequate | Needs Improvement
--- | --- | -- |
One or more data ethics challenges are identified. <br/> <br/> The case study clearly describes a real-world incident reflecting that challenge, and highlights undesirable consequences or harms it caused. <br/><br/> There is at least one linked resource to prove this occurred. | One data ethics challenge is identified. <br/><br/> At least one relevant harm or consequence is discussed briefly. <br/><br/> However discussion is limited or lacks proof of real-world occurence. | A data challenge is identified. <br/><br/> However the description or resources do not adequately reflect the challenge or prove it's real-world occurence. |

## Acknowledgments

Thanks to Microsoft for creating the open-source course [Data Science for Beginners](https://github.com/microsoft/Data-Science-For-Beginners). It inspires the majority of the content in this chapter.