*This session is facilitated by Lucy Mehrabyan, Maribeth Rauh *
About this session
The session will centre around the role of algorithms in evaluating job applicants. Majority of companies today use automated resume screening tools/recruiting tools where biases tend to sweep in (e.g. Amazon’s automated recruiting tool which had bias against women).
Through a simulation participants will take on a specific role: job applicant, hiring company, etc. From these different perspectives we will delve into learning about different definitions and metrics of fairness as well as evaluating tradeoffs in this specific context.
Goals of this session
The goal of the session is to convey in a concrete way what “fairness” in AI actually means through a case study approach with role play. Our envisioned outcome is for our participants to 1) understand definitions and metrics of fairness 2) be able to assess tradeoffs when making decisions about fairness.