Responsible AI

Course Description

Overview. The development and deployment of Artificial Intelligence (AI) systems increasingly involves choices that impact people and society at large. The responsible development and use of AI requires the integration of ethical, societal, and policy considerations into the use of AI tools and the development of AI systems in high-stakes domains, such as employment, education, lending, criminal justice, medicine, and beyond. We will discuss:

1. the pathways through which AI can lead to or amplify problematic decision-making practices (e.g., discrimination, inscrutability, invasion of privacy, and beyond);
2. recent technological methods and remedies to capture and alleviate these concerns; and
3. the scope of applicability and limitations of technological remedies in the context of several contemporary application domains.

The course's primary goals are: (a) to raise awareness about the social, ethical, and policy implications of AI, and (b) to prepare students to critically analyze these issues as they emerge in the ever-expanding use of AI in socially consequential domains.
This course will cover:

• Ethical concepts such as: respect for justice, fairness, bias, and discrimination, coercion, deception, exploitation, domination
• Principles and requirements of responsible AI including: and the principles of responsible AI including: transparency, explainability, validity, reliability, robustness, privacy, accountability, responsibility
• Organizational challenges to implementing ethical and responsible AI practices

This spreadsheet outlines the main modules of the course and the tentative topics that may be discussed in each one.

Prerequisite knowledge. Familiarity with AI/ML is a prerequisite. In particular, students are expected to be familiar with basic concepts in probability, statistics, and optimization.
Prerequisite/Corequisites/Antirequisite Courses. Only for students enrolled in 10-735: (15-122 Principles of Imperative Computation) and (21-128 or 21-127 or 15-151 Concepts of Math) and (36-218 or 36-217 or 15-259 or 36-235 or 36-225 or 15-359 or 21-325 or 36-219 Probability)

Performance Assessment. Assessment will be based on the following deliverables:

• Participation in class-room activities (20%)
• Homework assignments (30%)
• Canvas quizzes (10%)
• Final team project (40%)
• Extra credit (10%)

For a more detailed syllabus, please view this document. (Note: you need to be signed in via your CMU credentials to be able to access the document).

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