Ethics in Data, Data Science, and AI for Public Policy

Catalog description

As data science technology takes an ever-increasing role in our lives, it seems that progress frequently outpaces ethical considerations and regulation. The news is awash with stories of tech companies’ ethical failures in their acquisition, storage, and use of information. This course will begin with an examination of ethical concerns associated with data science, including biases, privacy, surveillance, discrimination, transparency, and accountability. The second half of the course will cover AI. The growing deployment of large language models (LLMs) has introduced a plethora of new ethical problems including how LLMs are trained, algorithmic bias, user addiction and misuse, accidental disclosure of confidential information, and many more problems. The course will look at several real-life ethical failures so that we may assess what went wrong and what steps need to be taken to prevent future harm.