Fairness in Machine Learning

Abstract: Machine learning models are becoming the main tools for addressing complex societal problems and are also increasingly deployed to make or support decisions about individuals in many consequential areas of their lives, from justice to healthcare. Therefore, the ethical implications of such decisions, including concepts such as privacy, transparency, accountability, reliability, autonomy, and fairness need to be taken into account. Specifically, we will explain the current landscape in AI Fairness, from the sources of the bias and different algorithmic fairness approaches to their limitations and cutting-edge approaches. The main goal is to provide a general overview of what is Fairness as well as the main research challenges that the community has to address.

Short bio: Adrián Arnaiz Rodríguez is an ELLIS PhD student. He holds a Bachelor’s degree in Computer Engineering (2019, Universidad de Burgos) and a Master’s degree in Data Science and Artificial Intelligence (2021, Universitat Oberta de Catalunya) doing the MSc thesis with Baris Kanber (University College London) in medical neuroimaging. His PhD supervisors are Nuria Oliver (ELLIS Alicante), Francisco Escolano (Universidad de Alicante) and Manuel Gómez Rodríguez (Max Planck Institute for Software Systems). His PhD topics are AI Fairness, Causality and Graph Theory to enhance ethics, accountability, and transparency in algorithmic decision-making.

Presenter: Adrián Arnaiz Rodríguez

Date: 2022-01-17 10:00 (CET)

Location: Salon de Actos Politecnica IV, Carretera San Vicente del Raspeig s/n, San Vicente del Raspeig 03690, Alicante ES