This article provides a comprehensive survey of bias mitigation methods for achieving
fairness in Machine Learning (ML) models. We collect a total of 341 publications concerning …

Automating decision systems has led to hidden biases in the use of artificial intelligence (AI).
Consequently, explaining these decisions and identifying responsibilities has become a …

Data-driven algorithms are studied and deployed in diverse domains to support critical
decisions, directly impacting people's well-being. As a result, a growing community of …

Abstract Fairness in Machine Learning (ML) has emerged as a crucial concern as these
models increasingly influence critical decisions in various domains, including healthcare …

As Artificial Intelligence (AI) continues to advance rapidly, it becomes increasingly important
to consider AI's ethical and societal implications. In this paper, we present a bottom-up …

Robustness and fairness are two equally important issues for machine learning systems.
Despite the active research on robustness and fairness of ML recently, these efforts focus on …

Existing research literally evaluates model fairness over limited observed data. In practice,
however, factors such as maliciously crafted examples and naturally corrupted examples …

Calibration is a well-studied property of predictors which guarantees meaningful uncertainty
estimates. Multicalibration is a related notion--originating in algorithmic fairness--which …

Three key properties that are desired of trustworthy machine learning models deployed in
high-stakes environments are fairness, explainability, and an ability to account for various …

Abstract Machine learning models are deployed as a central component in decision making
and policy operations with direct impact on individuals' lives. In order to act ethically and …