Fair Decision-making Under Uncertainty
This work addresses fairness concerns in socially sensitive applications like marketing and recidivism prediction, but it is incremental as it builds on existing fairness research by incorporating uncertainty and censorship.
The paper tackles the problem of ensuring fairness in AI decision-making under uncertainty, particularly in scenarios with censored data, by proposing new fairness notions and a framework that measures and mitigates discrimination, as demonstrated through empirical evaluations on real-world datasets.
There has been concern within the artificial intelligence (AI) community and the broader society regarding the potential lack of fairness of AI-based decision-making systems. Surprisingly, there is little work quantifying and guaranteeing fairness in the presence of uncertainty which is prevalent in many socially sensitive applications, ranging from marketing analytics to actuarial analysis and recidivism prediction instruments. To this end, we study a longitudinal censored learning problem subject to fairness constraints, where we require that algorithmic decisions made do not affect certain individuals or social groups negatively in the presence of uncertainty on class label due to censorship. We argue that this formulation has a broader applicability to practical scenarios concerning fairness. We show how the newly devised fairness notions involving censored information and the general framework for fair predictions in the presence of censorship allow us to measure and mitigate discrimination under uncertainty that bridges the gap with real-world applications. Empirical evaluations on real-world discriminated datasets with censorship demonstrate the practicality of our approach.