CS 5/692, Winter 2020
Computer science research has changed dramatically over the last ten years, both in terms of the ways in which it is conducted as well as the ends to which it is applied. Research in our field is powered by large quantities of data- Tweets, clicks, records, posts, etc.- which, as a rule, were all created by somebody, somewhere. Furthermore, the algorithms and computational techniques that we develop are finding their way into every corner of our lives. They determine what news we see, affect our financial and professional choices, and are widely used as part of our criminal justice system. As they do so, they interact with all aspects of our society, flattening some forms of inequality while amplifying others, often in subtle and surprising ways. Seemingly-minor methodological choices by system designers can have profound consequences. This course will explore these and other issues, with the goal of preparing researchers-in-training with the knowledge they need to responsibly conduct research in this area, and also to prepare them for their professional careers.
Topics will include traditional RCR subjects such as informed consent, ethical reasoning, conflicts of interest, etc., but will address these issues through the lens of modern machine learning and “big data” problems. Additional topics will include issues of algorithmic bias, anonymity, data privacy, value-sensitive design, fairness/accountability/transparency, etc.
By the end of the course, students will be:
Week 1–5: Principles of ethical reasoning; policy; human subjects & privacy; design principles (value-sensitive design); case study methodology;
Week 6–8: Algorithms, applications, and bias; fairness, accountability, transparency
Week 9-11: dataset documentation? handling sensitive data? professional ethics in CS/AI/ML; ethical codes;
Mondays, 14:00 in CDRC 3200
Starts: January 6
Ends: March 13
Instructor: Steven Bedrick
Office Location: Gaines Hall, 21
Office Hours: Wednesday, 9:00-10:30 AM