Speech given at Int. Conference on Artificial Intelligence: Risks, Regulations and Roadmaps, Dec 7th 2023. The conference addresses the balancing act of regulation: seeking to ensure neither too little nor too much. While we'll present and discuss major societal threats posed by AI, we'll also consider the missed opportunities if stringent regulations hinder AI-based scientific, economic, and general human progress.

Short abstract
The use of machine learning-based algorithms in medicine can exacerbate existing inequalities, and blatantly discriminatory algorithms have been revealed in recent years. Simultaneously, a wide range of technical methods has been developed to limit and control bias, and there are various definitions of what constitutes fairness in classification.

The lecture will describe dilemmas in the allocation of patients in critical situations (e.g., organ donation) in relation to classification principles. Three influential principles for regulating bias will be highlighted—principles that are individually plausible and ethically defensible but cannot be fulfilled simultaneously. Therefore, it is not possible to base a fair distribution of goods and burdens on known bias control principles, as they do not take into account mechanisms behind social and medical differences in the patient population.

Bio
Jonathan Patscheider, M.Eng. from DTU, completed a master's thesis last year on the Fairness of AI-based algorithms. Jonathan Patscheider has worked as a Special Advisor in the Ministry of Foreign Affairs’, and now currently serves a Vice President for Trust Stamp. Concurrently, Jonathan passionately engages in AI fairness research projects with different research institutions, exemplifying his multifaceted expertise.