Responsible AI: Privacy, Fairness, and Robustness Seminar

Course Description

This seminar-style course delves into the ethical dimensions of Artificial Intelligence (AI), with a particular focus on the intersectionality of privacy, fairness, and robustness. The course is structured around reading, discussing, and critically analyzing seminal and state-of-the-art papers in the field. Participants will engage in intellectual discourse to understand the challenges, methodologies, and emerging trends related to responsible AI. The course is designed for graduate students with good ML, stats, and optimization background.

Course Objectives

• Critically assess and discuss the literature on privacy, fairness, and robustness in AI.
• Identify challenges and propose potential solutions for responsible AI.
• Foster interdisciplinary discussions to explore the ethical dimensions of AI.
• Engage in a deep intellectual exploration of the field through paper discussions and presentations.

Prerequisites

• Basic understanding of machine learning.
• Basic understanding of optimization.

Syllabus

TBA

Assessment

• Paper Summaries: 40%
• Blogging and Participation: 30%
• Final Presentation: 30%

Recommended Reading

• A curated list of papers will be provided at the start of the course.

Instructor

Ferdinando Fioretto Assistant Professor in Computer Science University of Virgina

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This syllabus is subject to changes to meet the learning needs of the course participants.