Fairness for Affective and Wellbeing Computing Systems and Agents

Explore fairness considerations in affective and wellbeing computing systems through this distinguished lecture by Hatice Gunes from the University of Cambridge. Delve into the critical intersection of artificial intelligence, emotion recognition, and ethical computing as the speaker examines how bias and fairness issues manifest in systems designed to understand and respond to human emotions and wellbeing. Learn about the challenges of developing equitable AI agents that can accurately interpret affective states across diverse populations while avoiding discriminatory outcomes. Discover current research methodologies for identifying and mitigating bias in emotion recognition algorithms, wellbeing assessment tools, and related computational systems. Gain insights into the technical and ethical frameworks needed to ensure that affective computing technologies serve all users fairly, regardless of demographic characteristics or cultural backgrounds. Understand the implications of unfair affective computing systems on vulnerable populations and explore potential solutions for creating more inclusive and equitable AI-driven wellbeing technologies.