Long Term Fairness

Long-term fairness in machine learning and decision-making systems focuses on mitigating the long-term, often unintended, consequences of seemingly fair algorithms. Current research emphasizes developing models and algorithms, such as those based on Markov Decision Processes and deep generative models, that explicitly consider the temporal evolution of fairness metrics and the interplay between decisions and evolving data distributions. This research is crucial for addressing ethical concerns and ensuring equitable outcomes in applications ranging from resource allocation and hiring to loan approvals and criminal justice, where algorithmic decisions have lasting societal impacts.