DATA AND ALGORITHMS: ETHICS AND POLICY

PHILOS 244

Introduction to contemporary moral and political issues in Artificial Intelligence (AI) and Data Ethics, integrating urgent problems, controversies, and continuously updated case studies. Introduces basic technical concepts such as the bias/variance tradeoff, the reference class problem, and inductive risk. Covers topics such as data and privacy, the impacts of automation on society, and the use of algorithms in medicine and criminal law.

BMI 738

Designed to educate data scientists, particularly those who work with biomedical data, about ethical and regulatory issues that may arise in the course of their research and professional interactions.

LIS 661

Overview of modern ethical theories and how they inform information agency policies and practices; examines selected policy issues relating to information and communications; includes topics such as intellectual property, privacy, censorship, equity of access.

ECE/ISYE 570

Introduction to ethical issues in data engineering and principled solutions. Algorithmic fairness (individual fairness, group fairness, counterfactual fairness), differential privacy and its applications, and robustness.

LIS 408

Introductory exploration of generative artificial intelligence (AI) from a strategic information studies perspective, such as emphasizing values of privacy, data stewardship, and UX. Topics include societal, legal, policy, and ethical issues related to generative AI, and practicing the ability to evaluate, plan for, and securely work with these digital technologies. Focus on the strategies, skills and self-sufficiency necessary to evaluate, implement, and critique various and evolving AI tools.