M. Elizabeth Karns is a lawyer and epidemiologist. Her teaching is directly connected to her practice, which has focused on statistical evidence, occupational illnesses, assault and harassment injuries, and reproductive harms. Professor Karns has been involved in every aspect of the data life cycle, from initial design to post-event forensic evaluations. Her work on data science ethics brings together professional practice norms, individual values, and organizational goals. Professor Karns is currently developing a virtual reality experience based on the unintended consequences of automated fraud detection.
Course Overview
When a data project leaves your hands, the ethical choices you made will travel with it, and those choices can sometimes lead to significant consequences. In this course, you will apply your knowledge to situations where seemingly small ethical choices made by individuals result in large, “macro-ethics” problems of fairness, justice, privacy, and consent. You will trace the data science lifecycle to anticipate consequences and discuss the importance of transparency and accountability in your work. Finally, you will practice applying moral imagination to a data lifecycle and ecosystem then develop recommendations for monitoring and intervention based on that context. By recognizing the connections between desk-level choices and world-level impacts, you will acquire the skill to move your data science work in a positive direction.
Key Course Takeaways
- Recognize the macro-level ethical problems of bias, fairness, and justice in data science
- Include consent, privacy, and safety as significant aspects of data science work
- Promote transparency as an aspect of accountability
- Evaluate an outcome's unintended consequences and ethical choice points
- Trace the data science lifecycle to anticipate consequences
- Practice applying moral imagination to a data lifecycle and ecosystem
- Develop recommendations for monitoring and intervention
How It Works
Course Length
2 weeks
Effort
3 to 5 hours of study per week
Format
100% online, instructor-led
Course Author
Senior Lecturer, Social Statistics, Cornell ILR School
Who Should Enroll
- Data and machine learning scientists
- Data bank managers and curators
- Model and algorithm developers
- Data protection managers and privacy stewards
- Ethics officers
- Statisticians
- IT managers and technology consultants
- Data and business analysts
- Software developers and programmers
- Quality and security engineers
- Compliance professionals
- Lawyers
Get It Done
100% Online
100% Online
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