To mitigate the bias exhibited by machine learning models, fairness criteria can be
integrated into the training process to ensure fair treatment across all demographics, but it …

Long-term fairness is an important factor of consideration in designing and deploying
learning-based decision systems in high-stake decision-making contexts. Recent work has …

Neglecting the effect that decisions have on individuals (and thus, on the underlying data
distribution) when designing algorithmic decision-making policies may increase inequalities …

Recent research has shown that seemingly fair machine learning models, when used to
inform decisions that have an impact on peoples' lives or well-being (eg, applications …

Decisions made by machine learning models may have lasting impacts over time, making
long-term fairness a crucial consideration. It has been shown that when ignoring the long …

In sequential decision-making problems involving sensitive attributes like race and gender,
reinforcement learning (RL) agents must carefully consider long-term fairness while …

The widespread integration of Machine Learning systems in daily life, particularly in high-
stakes domains, has raised concerns about the fairness implications. While prior works have …

Repeated risk minimization is a popular choice in real-world recommender systems driving
their recommendation algorithms to adapt to user preferences and trends. However …

Fair and Optimal Prediction via Post-Processing Page 1 Fair and Optimal Prediction via Post-Processing
Han Zhao Department of Computer Science, University of Illinois Urbana-Champaign, Urbana …

Equipping current decision-making tools with notions of fairness, equitability, or other
ethically motivated outcomes, is one of the top priorities in recent research efforts in machine …