As machine learning methods gain prominence within clinical decision-making, the need to
address fairness concerns becomes increasingly urgent. Despite considerable work …

AI-driven decision-making can lead to discrimination against certain individuals or social
groups based on protected characteristics/attributes such as race, gender, or age. The …

In the last few years, we have witnessed a surge in scholarly interest and scientific evidence
of how algorithms can produce discriminatory outcomes, especially with regard to gender …

Organizations are increasingly employing complex black-box machine learning models in
high-stakes decision-making. A popular approach to addressing the problem of opacity of …

Ethics-based auditing (EBA) is a structured process whereby an entity's past or present
behaviour is assessed for consistency with moral principles or norms. Recently, EBA has …

Efforts to promote equitable public policy with algorithms appear to be fundamentally
constrained by the “impossibility of fairness”(an incompatibility between mathematical …

Stereotypes, bias, and discrimination have been extensively documented in Machine
Learning (ML) methods such as Computer Vision (CV)[18, 80], Natural Language …

Information privacy is in trouble. Contemporary information privacy protections emphasize
individuals' control over their own personal information. But machine learning, the leading …

Algorithmic fairness is frequently motivated in terms of a trade-off in which overall
performance is decreased so as to improve performance on disadvantaged groups where …

Concerns regarding unfairness and discrimination in the context of artificial intelligence (AI)
systems have recently received increased attention from both legal and computer science …