As the integration of artificial intelligence and data science into society accelerates, ensuring fairness becomes paramount. This session delves into the intersection of algorithmic fairness and fair division arising in economic systems, computational social science, and others. Fair division aims to divide resources, tasks, benefits etc among people such that everyone involved feels they have received their fair share, even if they do not get the same amount. This concept is used in various areas, from splitting assets and liabilities among ex-partners to distributing refugees among EU countries.

Algorithmic fairness refers to making sure that algorithms and computer systems make decisions in a way that is fair and unbiased. This means that the outcomes or recommendations from these algorithms should not favor or discriminate against any particular group of people based on factors like race, gender, or age. The aim is to create systems that treat everyone equitably and justly.

Program

Equilibrium Screening and Categorical Inequality
Mogens Fosgerau, Professor, Department of Economics, University of Copenhagen, 30 min.

Why does inequality increase?
Birthe Larsen, Associate Professor, Department of Economics, CBS, 30 min.

FairMatch: Multi-stakeholder Fairness for Algorithmic Hiring
Mesut Kaya, Postdoctoral Researcher at JobIndex A/S and IT University Copenhagen, 30 min.

Organizers

• Ioannis Caragiannis, Professor, Department of Computer Science, Aarhus University
• Ira Assent, Professor, Department of Computer Science, Aarhus University

Level

Introductory: Suitable for beginners with little to no prior knowledge of the subject.