Sym. Papadopoulos

Uploaded by AUTH

AI bias is an emerging concern in the field of AI, due to the widespread deployment of AI-based services and applications with ubiquitous effects on our daily lives. AI bias is particularly important in high-stakes decision making scenarios such as CV ranking and hiring, credit scoring and recidivism prediction, but it is also becoming a growing concern in the context of generative AI systems, where harmful stereotypes are perpetuated and amplified through modern foundational This tutorial will introduce the emerging field of AI bias and fairness, starting from the general AI setting, and then will proceed with a more in-depth study of the problem in the context of computer vision models and applications. It will also include two hands- on interactive sessions, where participants will have the opportunity to experiment with methods for assessing and mitigating bias, first in general tabular datasets and then in computer vision datasets and models.

This session consists of four talks:

1.”Introduction to AI bias and fairness”, E. Ntoutsi (40 min)
2. “Bias in Computer Vision”, S. Papadopoulos (40 min)
3. “Overview of visual bias mitigation approaches”, C. Diou (40 min)
4. “Bias assessment and mitigation using FairBench: case study on visual data (hands-on)”, E. Krasanakis, G. Sarridis (2 hours)