HMI Data, AI & Society Seminar 09 ‘Incorporating Fairness in Two-Sided Online Platforms’

‘Incorporating Fairness in Two-Sided Online Platforms’ In this paper, Abhijnan discusses how algorithmic decision making is increasingly being used to assist or replace human decision making in domains with high societal impact, such as banking (estimating creditworthiness), recruiting (ranking job applicants), judiciary (offender profiling) and healthcare (identifying high-risk patients who need additional care). Consequently, in recent times, multiple research works have uncovered the potential for bias (unfairness) in algorithmic decisions in different contexts, and proposed mechanisms to control (mitigate) such biases. However, the emphasis of existing works has largely been on fairness in supervised classification or regression tasks, and fairness issues in other scenarios remain relatively unexplored. In this talk, he will cover recent work incorporating fairness in recommendation and matching algorithms in multi-sided platforms, where the algorithms need to fairly consider the preferences of multiple stakeholders. Examples of such platforms include ecommerce sites, ride-hailing applications, content streaming platforms or even online charities. He will discuss the notions of fairness in these contexts and propose techniques to achieve them. The talk will be concluded with a list of open questions and directions for future work.