People Are Not Coins - Morally Distinct Types of Predictions Necessitate Different Fairness Constraints

Explore a thought-provoking 15-minute conference talk that challenges conventional notions of fairness in predictive systems. Delve into the argument presented by Eleonora Viganó, Corinna Hertweck, Christoph Heitz, and Michele Loi that different types of predictions require distinct fairness constraints. Examine the analogy of people not being coins to understand why a one-size-fits-all approach to fairness in AI and machine learning may be inadequate. Gain insights into the moral implications of various prediction types and how they necessitate tailored approaches to ensure ethical and equitable outcomes in algorithmic decision-making processes.