Why Fairness Cannot Be Automated: Bridging the Gap Between EU Non-Discrimination Law and AI

• URL: http://arxiv.org/abs/2005.05906v1
• Date: Tue, 12 May 2020 16:30:12 GMT
• Title: Why Fairness Cannot Be Automated: Bridging the Gap Between EU Non-Discrimination Law and AI
• Authors: Sandra Wachter, Brent Mittelstadt, Chris Russell
• Abstract summary: Article identifies a critical incompatibility between European notions of discrimination and existing statistical measures of fairness. We show how the legal protection offered by non-discrimination law is challenged when AI, not humans, discriminate. We propose "conditional demographic disparity" (CDD) as a standard baseline statistical measurement.
• Score: 10.281644134255576
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This article identifies a critical incompatibility between European notions of discrimination and existing statistical measures of fairness. First, we review the evidential requirements to bring a claim under EU non-discrimination law. Due to the disparate nature of algorithmic and human discrimination, the EU's current requirements are too contextual, reliant on intuition, and open to judicial interpretation to be automated. Second, we show how the legal protection offered by non-discrimination law is challenged when AI, not humans, discriminate. Humans discriminate due to negative attitudes (e.g. stereotypes, prejudice) and unintentional biases (e.g. organisational practices or internalised stereotypes) which can act as a signal to victims that discrimination has occurred. Finally, we examine how existing work on fairness in machine learning lines up with procedures for assessing cases under EU non-discrimination law. We propose "conditional demographic disparity" (CDD) as a standard baseline statistical measurement that aligns with the European Court of Justice's "gold standard." Establishing a standard set of statistical evidence for automated discrimination cases can help ensure consistent procedures for assessment, but not judicial interpretation, of cases involving AI and automated systems. Through this proposal for procedural regularity in the identification and assessment of automated discrimination, we clarify how to build considerations of fairness into automated systems as far as possible while still respecting and enabling the contextual approach to judicial interpretation practiced under EU non-discrimination law. N.B. Abridged abstract

Related papers

• Formalising Anti-Discrimination Law in Automated Decision Systems [1.560976479364936]
  We study the legal challenges in automated decision-making by analysing conventional algorithmic fairness approaches. By translating principles of anti-discrimination law into a decision-theoretic framework, we formalise discrimination. We propose a new, legally informed approach to developing systems for automated decision-making.
  arXiv  Detail & Related papers  (2024-06-29T10:59:21Z)
• Auditing for Racial Discrimination in the Delivery of Education Ads [50.37313459134418]
  We propose a new third-party auditing method that can evaluate racial bias in the delivery of ads for education opportunities. We find evidence of racial discrimination in Meta's algorithmic delivery of ads for education opportunities, posing legal and ethical concerns.
  arXiv  Detail & Related papers  (2024-06-02T02:00:55Z)
• Unlawful Proxy Discrimination: A Framework for Challenging Inherently Discriminatory Algorithms [4.1221687771754]
  EU legal concept of direct discrimination may apply to various algorithmic decision-making contexts. Unlike indirect discrimination, there is generally no 'objective justification' stage in the direct discrimination framework. We focus on the most likely candidate for direct discrimination in the algorithmic context.
  arXiv  Detail & Related papers  (2024-04-22T10:06:17Z)
• Non-discrimination law in Europe: a primer for non-lawyers [44.715854387549605]
  We aim to describe the law in such a way that non-lawyers and non-European lawyers can easily grasp its contents and challenges. We introduce the EU-wide non-discrimination rules which are included in a number of EU directives. The last section broadens the horizon to include bias-relevant law and cases from the EU AI Act, and related statutes.
  arXiv  Detail & Related papers  (2024-04-12T14:59:58Z)
• Implications of the AI Act for Non-Discrimination Law and Algorithmic Fairness [1.5029560229270191]
  The topic of fairness in AI has sparked meaningful discussions in the past years. From a legal perspective, many open questions remain. The AI Act might present a tremendous step towards bridging these two approaches.
  arXiv  Detail & Related papers  (2024-03-29T09:54:09Z)
• Evaluating the Fairness of Discriminative Foundation Models in Computer Vision [51.176061115977774]
  We propose a novel taxonomy for bias evaluation of discriminative foundation models, such as Contrastive Language-Pretraining (CLIP) We then systematically evaluate existing methods for mitigating bias in these models with respect to our taxonomy. Specifically, we evaluate OpenAI's CLIP and OpenCLIP models for key applications, such as zero-shot classification, image retrieval and image captioning.
  arXiv  Detail & Related papers  (2023-10-18T10:32:39Z)
• Compatibility of Fairness Metrics with EU Non-Discrimination Laws: Demographic Parity & Conditional Demographic Disparity [3.5607241839298878]
  Empirical evidence suggests that algorithmic decisions driven by Machine Learning (ML) techniques threaten to discriminate against legally protected groups or create new sources of unfairness. This work aims at assessing up to what point we can assure legal fairness through fairness metrics and under fairness constraints. Our experiments and analysis suggest that AI-assisted decision-making can be fair from a legal perspective depending on the case at hand and the legal justification.
  arXiv  Detail & Related papers  (2023-06-14T09:38:05Z)
• Algorithmic Unfairness through the Lens of EU Non-Discrimination Law: Or Why the Law is not a Decision Tree [5.153559154345212]
  We show that EU non-discrimination law coincides with notions of algorithmic fairness proposed in computer science literature. We set out the normative underpinnings of fairness metrics and technical interventions and compare these to the legal reasoning of the Court of Justice of the EU. We conclude with implications for AI practitioners and regulators.
  arXiv  Detail & Related papers  (2023-05-05T12:00:39Z)
• Fair Machine Learning in Healthcare: A Review [90.22219142430146]
  We analyze the intersection of fairness in machine learning and healthcare disparities. We provide a critical review of the associated fairness metrics from a machine learning standpoint. We propose several new research directions that hold promise for developing ethical and equitable ML applications in healthcare.
  arXiv  Detail & Related papers  (2022-06-29T04:32:10Z)
• Measuring Fairness of Text Classifiers via Prediction Sensitivity [63.56554964580627]
  ACCUMULATED PREDICTION SENSITIVITY measures fairness in machine learning models based on the model's prediction sensitivity to perturbations in input features. We show that the metric can be theoretically linked with a specific notion of group fairness (statistical parity) and individual fairness.
  arXiv  Detail & Related papers  (2022-03-16T15:00:33Z)
• Statistical discrimination in learning agents [64.78141757063142]
  Statistical discrimination emerges in agent policies as a function of both the bias in the training population and of agent architecture. We show that less discrimination emerges with agents that use recurrent neural networks, and when their training environment has less bias.
  arXiv  Detail & Related papers  (2021-10-21T18:28:57Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.