Program

 

 Schedule

(Room# Almaty 6005)

9:25-9:30 Welcoming the participants


 9:30-10:30 Keynote by Haris Aziz


10:30-11:00 Coffee break + Posters


11:00-12:30 Technical sessions (6 talks 15 mins each)

Session Chair: Toby Walsh


12:30-14:00 Lunch


14:00-15:00 Keynote by Lirong Xia


15:00-16:00 Coffee break + Posters


16:00-17:00 Technical sessions (4 talks 15 mins each)

Session Chair: Ioannis Caragiannis


17:00-18:30 Posters

 Invited Speakers

Lirong Xia

Haris Aziz

Most Equitable Voting Rules

Lirong Xia

Abstract

How can we design the most equitable voting rules? The answer critically depends on the definition of equity and fairness. Among all axioms, anonymity (all agents being treated equally) and neutrality (all alternatives being treated equally) are widely regarded as “minimal demands” and “uncontroversial". Unfortunately, the ANR impossibility—there is no voting rule that satisfies anonymity, neutrality, and resolvability (always choosing one decision)—holds even in the simple setting of two decisions and two agents. How to design voting rules that optimally satisfy anonymity, neutrality, and resolvability remains an open question.


We address this question for a wide range of preferences and decisions that include ranked lists and committees, which cover many group decision making scenarios such as (approval) voting, rank aggregation, and (approval-based) multi-winner elections. We propose a novel and strong notion called most equitable refinements, which enables us to develop a group-theoretic approach that addresses the optimal design question via the lens of characterization, algorithm design, and likelihood analysis. These results answer several open questions and shed new lights on fighting fundamental impossibilities of equity and fairness.


Part of the talk is based on https://arxiv.org/abs/2205.14838

Bio

Lirong Xia is an associate professor in the Department of Computer Science at Rensselaer Polytechnic Institute (RPI). He was an NSF CI Fellow at the Center for Research on Computation and Society at Harvard University. He received his Ph.D. in Computer Science and M.A. in Economics from Duke University. His research focuses on the intersection of computer science and microeconomics. He is the recipient of an NSF CAREER award, a Rensselaer James M. Tien'66 Early Career Award, and was named as one of "AI's 10 to watch" by IEEE Intelligent Systems. 

Slides


CFD-Lirong.pptx

Best of Both Worlds Fairness

Haris Aziz

Abstract

Best of both worlds fairness is a paradigm in which the goal is to design randomised algorithms that simultaneously achieve desirable fairness properties ex-post and ex-ante. In this talk, I will discuss recent results on best of both worlds fairness including some brand-new work on best of both worlds fairness in committee voting and participatory budgeting.

Bio

Haris Aziz is a Scientia Associate Professor at UNSW Sydney and the Director of the UNSW AI Institute. 

His research interests lie at the intersection of artificial intelligence, theoretical computer science and mathematical social sciences --- especially computational social choice and algorithmic game theory. The Australian newspaper named him as the national field leader in game theory and decision sciences in years 2020-2022. He is a recipient of the CORE Chris Wallace Research Excellence Award (2017). In 2016, he was selected by the Institute of Electrical and Electronics Engineers (IEEE) for the AI 10 to Watch List. 

Haris is a board member of International Foundation for Autonomous Agents and Multiagent Systems, editor of TheoretiCS and an associate editor of several journals, including Artificial Intelligence, Journal of Artificial Intelligence Research, Autonomous Agents and Multi-Agent Systems, and Social Choice & Welfare.

Slides


bothworlds_slides_IJCAI23.pdf