A central goal of algorithmic fairness is to reduce bias in automated decision making. An
unavoidable tension exists between accuracy gains obtained by using sensitive information …

Fairness-aware machine learning has attracted a surge of attention in many domains, such
as online advertising, personalized recommendation, and social media analysis in web …

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 …

Machine learning (ML) has become prominent in applications that directly affect people's
quality of life, including in healthcare, justice, and finance. ML models have been found to …

The field of automated machine learning (AutoML) introduces techniques that automate
parts of the development of machine learning (ML) systems, accelerating the process and …

One of the main concerns about fairness in machine learning (ML) is that, in order to achieve
it, one may have to trade off some accuracy. To overcome this issue, Hardt et al.(Adv Neural …

Bounding privacy leakage over compositions, ie, privacy accounting, is a key challenge in
differential privacy (DP). However, the privacy parameter ($\varepsilon $ or $\delta $) is often …

Motivated by concerns surrounding the fairness effects of sharing and transferring fair
machine learning tools, we propose two algorithms: Fairness Warnings and Fair-MAML. The …

Unwanted bias is a major concern in machine learning, raising in particular significant
ethical issues when machine learning models are deployed within high-stakes decision …

A recent trend of fair machine learning is to define fairness as causality-based notions which
concern the causal connection between protected attributes and decisions. However, one …