We're still actively developing this site. If you encounter any issues, please report them! - Report an issue

INTRODUCTORY ARTIFICIAL INTELLIGENCE (AI) AND DATA ETHICS

PHILOS 244
Course Description

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.

Prerequisties

Sophomore standing

Satisfies

This course does not satisfy any prerequisites.

Credits

3 to 4

Offered

Not Applicable

Grade Point Average
Not Reported

Could not calculate change

Completion Rate
Not Reported

Could not calculate change

A Rate
Not Reported

Could not calculate change

Class Size
Not Reported

No change from Historical

No data available

Instructors (2025 Fall)

Sorted by ratings from Rate My Professors

Similar Courses

DATA AND ALGORITHMS: ETHICS AND POLICY

LIS 461

An introduction to ethical, legal and policy issues related to analytics, "big data" and algorithms to support decision making. Gain familiarity with major debates and controversies in a variety of contexts. Critically analyze course materials and apply moral reasoning and legal concepts to assess case studies and critique arguments made by others.

ETHICS OF DATA FOR ENGINEERS

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.

SEMINAR-ETHICS

PHILOS 941

Seminar on ethics.

TRUSTWORTHY ARTIFICIAL INTELLIGENCE

COMPSCI/ECE 763

Explore security and privacy aspects of trustworthy artificial intelligence. Three core subjects will be considered: differential privacy and algorithmic fairness; adversarial machine learning; and end-to-end trustworthy systems. A selection of more advanced topics may be covered such as additional notions of privacy, language-based security, and robust optimization. Knowledge of probability/statistics (such as MATH 431), cryptography (such as MATH 435), security (such asCOMP SCI 642), and modern machine learning (such asM E/​COMP SCI/​E C E  539or540) is required.

INTRODUCTORY ETHICS

PHILOS 241

Nature of moral problems and of ethical theory, varieties of moral skepticism, practical ethics and the evaluation of social institutions.