In our CPEC Colloquium series, we invite distinguished speakers to share their research on perspicuity and related topics. We’re happy to announce the following talk:
Friday, Feb 19, 2:15pm: Prof. Isabel Valera (Saarland University, Saarbrücken, DE)
Fair & Interpretable ML – Challenges and recent advances
Algorithmic decision making processes are increasingly becoming automated and data-driven in both online (e.g., spam filtering, product personalization), as well as offline (e.g., pretrial risk assessment, mortgage approvals) settings. However, as automated data analysis supplements and even replaces human supervision in decision making, there are growing concerns from civil organizations, governments, and researchers about potential unfairness and lack of transparency of these algorithmic systems. To address these concerns, the emerging field of ethical machine learning has focused on proposing definitions and mechanisms to ensure the fairness and explicability of the outcomes of these systems. However, as we will show in this talk, these solutions are still far from being perfect, and thus, implementable in practice. This talk will summarize the recent advances on how to ensure fairness and explicability of the outcomes of such algorithmic decision making systems, as well as the open challenges still to be addressed in this context. Specifically, I will show in order for ethical ML, it is essential to have a holistic view of the algorithm – starting from the data collection process before training, all the way to the deployment of the system in the real-world.