## Write A Data Ethics Case Study ## Instructions You've learned about various [Data Ethics Challenges](README?id=_2-ethics-challenges) and seen some examples of [Case Studies](README?id=_3-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 [the lesson examples](README?id=_2-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, online research paper etc.) to prove this was a real-world occurrence. Bonus points: share resources that also showcase the potential harms & consequences from the incident, or highlight positive steps taken to prevent its recurrence. ## Rubric Exemplary | Adequate | Needs Improvement --- | --- | -- | One or more data ethics challenges are identified.

The case study clearly describes a real-world incident reflecting that challenge, and highlights undesirable consequences or harms it caused.

There is at least one linked resource to prove this occurred. | One data ethics challenge is identified.

At least one relevant harm or consequence is discussed briefly.

However discussion is limited or lacks proof of real-world occurence. | A data challenge is identified.

However the description or resources do not adequately reflect the challenge or prove it's real-world occurence. |