On the Relationship of Fairness and Uncertainty

Abstract: To ensure the reliability and trustworthiness of machine learning models in real-world applications, it's crucial to assess the uncertainty about their predictions (predictive uncertainty). In a practical scenario, uncertain decisions will be deferred to human experts or opted for a failsafe alternative. Machine learning models already operational in our society to make inferences or predictions in consequential domains in people's lives ---such as healthcare, access to credit, social programs or promotions--- have been found to exhibit differences in their performance depending on the person's gender, race, or age, leading to unfair treatment to certain groups of individuals. Fairness research aims to identify, analyze, and address these systematic biases in machine learning models. This presentation will delve into the interplay between predictive uncertainty and algorithmic fairness, addressing questions such as: Do models exhibit the same levels of unfairness at high or low certainty levels? What kind of uncertainty leads to unfairness? Is it possible to leverage the predictive uncertainty to develop fairer models? I will provide a literature review of current knowledge about those questions, present our latest experimental insights, and discuss future research avenues.

Short bio: Kajetan Schweighofer is an ELLIS PhD student and holds a bachelor’s and master’s degree in physics as well as a second master’s degree in artificial intelligence from Johannes Kepler University Linz. The master’s thesis in artificial intelligence supervised by Dr. Sepp Hochreiter focused on Offline Reinforcement Learning. The master’s thesis in physics supervised by Dr. Armando Rastelli focused on utilizing artificial intelligence for quantum optical experiments. Kajetan conducts his PhD under the supervision of Dr. Sepp Hochreiter at Johannes Kepler University Linz and Dr. Nuria Oliver at ELLIS Alicante. He focuses on improving uncertainty quantification for deep learning methods, to make those methods reliable and trustworthy when applied in critical settings. Furthermore, during the exchange in Alicante throughout the winter semester 2023/24, Kajetan investigates implications of uncertainty on the fairness of deep learning methods.

Presenter: Kajetan Scheweighofer

Date: 2024-03-26 12:00 (CET)

Location: Oficinas ELLIS Alicante, Muelle Pte., 5 – Edificio A, Alicante 03001, Alicante ES