Exploring bias and fairness in artificial intelligence and machine learning algorithms

Fairness and Sequential Decision Making: Limits, Lessons, and Opportunities

“…Many real-world problems, such as autonomous driving, power grid management, wildfire fighting, military engagement, disaster relief, and inventory logistics, both fundamentally affect people's safety and access to resources and require sequential reasoning as they cannot be solved adequately via myopic decision making. However, although problems such as autonomous driving sometimes motivate the fairness literature [80,81,96,126,146,213,219], fairness conceptualizations and methods have largely been developed for predictive rather than sequential decision-making systems. Moreover, despite the fairness literature's acknowledgement of the long-term effects and sequential nature of many high-stakes decisions [36,55,68,74,75,95,98,107,143,152,158,175,193,194,202], including education and college admissions [5,101,163], recidivism risk prediction [62,147], predictive policing [48], child and homeless welfare [69,189], clinical trials [54], and hiring [33,144], work on these settings rarely engages problem formulations or approaches developed for sequential decision making, or efforts to conceptualize and address ethical concerns emerging from the ethical decision making literature.…”