PUBPOL 3725

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

Course Description

Course information provided by the 2025-2026 Catalog.

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.

Last 4 Terms Offered (None)

Learning Outcomes

• Analyze the lifecycle of a data science project, identify ethical concerns that might emerge at each stage of the project, and recommend measures for addressing these concerns.
• Apply best practices in data ethics in drafting an original data privacy policy directive memo for an organization.
• Discuss the pace of technological development in data science and regulatory policy governing this space.
• Analyze the development, training, deployment, use, and management of LLMs to develop best practices for minimizing ethical risks.

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