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Unlawful Proxy Discrimination: A Framework for Challenging Inherently Discriminatory Algorithms
In this paper, we focus on the most likely candidate for direct discrimination in the algorithmic context, termed inherent direct discrimination, where a proxy is inextricably linked to a protected characteristic.
The Neutrality Fallacy: When Algorithmic Fairness Interventions are (Not) Positive Action
While individuals and organizations have an obligation to avoid discrimination, the use of fairness-aware machine learning interventions has also been described as amounting to 'algorithmic positive action' under European Union (EU) non-discrimination law.
Algorithmic Unfairness through the Lens of EU Non-Discrimination Law: Or Why the Law is not a Decision Tree
In this paper, we aim to illustrate to what extent European Union (EU) non-discrimination law coincides with notions of algorithmic fairness proposed in computer science literature and where they differ.
Fairlearn: Assessing and Improving Fairness of AI Systems
Fairlearn is an open source project to help practitioners assess and improve fairness of artificial intelligence (AI) systems.
Can Fairness be Automated? Guidelines and Opportunities for Fairness-aware AutoML
The field of automated machine learning (AutoML) introduces techniques that automate parts of the development of machine learning (ML) systems, accelerating the process and reducing barriers for novices.
Does the End Justify the Means? On the Moral Justification of Fairness-Aware Machine Learning
In this paper, we present a framework for moral reasoning about the justification of fairness metrics and explore the moral implications of the use of fair-ml algorithms that optimize for them.
