DSC260: Data Science Ethics & Society

Instructor: Prof. David Danks

Prof. Danks' classes are incredibly interactive, and his charisma makes even the most complex concepts easy to understand. He simplifies difficult topics by using relatable analogies, which helped me grasp the material better.

The course covered a wide range of topics, including values, stakeholders, consent, algorithmic-justice, bias, privacy, and explainability. We also explored societal practices such as delegation, organizational incentives, and accountability, as well as governance mechanisms, including law, regulation, and norms.

These ethical concerns often go unchecked in data science projects. It is common for practitioners to simply source datasets from the web and use them directly in their work without fully understanding the potential ethical implications. This course has made me realize the importance of being aware of these issues when conducting data science and asking critical questions like:

• In what ways should companies be allowed to use the data they collect?
• How do these ethical concerns manifest at different stages of a data science effort, such as data collection, modeling/analysis, deployment, and usage?
• What societal-level issues arise when AI systems are used on a large scale?
• Who should be responsible when an algorithmic subject is at the mercy of decisions made by AI systems?

While the concepts were not difficult, there were too many things to remember, however, the assignments and project work ensured thinking and applying the ideas discussed in class.

Fall 2024