Fairness in Algorithmic Decision-making

2019

2020

An increasing number of decisions regarding the daily lives of human beings are being controlled by artificial intelligence (AI) algorithms in spheres ranging from healthcare, transportation, and education to college admissions, recruitment, provision of loans and many more realms. Since they now touch on many aspects of our lives, it is crucial to develop AI algorithms that are not only accurate but also objective and fair. Recent studies have shown that algorithmic decision-making may be inherently prone to unfairness, even when there is no intention for it. This paper presents an overview of the main concepts of identifying, measuring and improving algorithmic fairness when using AI algorithms. The paper begins by discussing the causes of algorithmic bias and unfairness and the common definitions and measures for fairness. Fairness-enhancing mechanisms are then reviewed and divided into pre-process, in-process and post-process mechanisms. A comprehensive comparison of the mechani...

SSRN Electronic Journal, 2020

In recent years a substantial literature has emerged concerning bias, discrimination, and fairness in AI and machine learning. Connecting this work to existing legal non-discrimination frameworks is essential to create tools and methods that are practically useful across divergent legal regimes. While much work has been undertaken from an American legal perspective, comparatively little has mapped the effects and requirements of EU law. This Article addresses this critical gap between legal, technical, and organisational notions of algorithmic fairness. Through analysis of EU non-discrimination law and jurisprudence of the European Court of Justice (ECJ) and national courts, we identify a critical incompatibility between European notions of discrimination and existing work on algorithmic and automated fairness. A clear gap exists between statistical measures of fairness as embedded in myriad fairness toolkits and governance mechanisms and the context-sensitive, often intuitive and ambiguous discrimination metrics and evidential requirements used by the ECJ; we refer to this approach as "contextual equality." This Article makes three contributions. 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. Many of the concepts fundamental to bringing a claim, such as the composition of the disadvantaged and advantaged group, the severity and type of harm suffered, and requirements for the relevance and admissibility of evidence, require normative or political choices to be made by the judiciary on a case-by-case

Philosophy & Technology, 2017

The combination of increased availability of large amounts of finegrained human behavioral data and advances in machine learning is presiding over a growing reliance on algorithms to address complex societal problems. Algorithmic decision-making processes might lead to more objective and thus potentially fairer decisions than those made by humans who may be influenced by greed, prejudice, fatigue, or hunger. However, algorithmic decision-making has been criticized for its potential to enhance discrimination, information and power asymmetry, and opacity. In this paper we provide an overview of available technical solutions to enhance fairness, accountability and transparency in algorithmic decision-making. We also highlight the criticality and urgency to engage multidisciplinary teams of researchers, practitioners, policy makers and citizens to co-develop, deploy and evaluate in the real-world algorithmic decision-making processes designed to maximize fairness and transparency. In doing so, we describe the Open Algortihms (OPAL) project as a step towards

2023

How to protect people from algorithmic harms? A promising solution, although in its infancy, is algorithmic impact assessment (AIA). AIAs are iterative processes used to investigate the possible short and long terms societal impacts of AI systems before their use, but with ongoing monitoring and periodic revisiting even after their implementation. When conducted in a participatory and transparent fashion, they could create bridges across the legal, social and computer science domains, promoting the accountability of the entity performing them as well as public scrutiny. They could enable to re-attach the societal and regulatory context to the mathematical definition of fairness, thus expanding the formalistic approach thereto. Whilst the regulatory framework in the European Union currently lacks the obligation to perform such AIA, some other provisions are expected to play a role in AI development, leading the way towards more widespread adoption of AIA. These include the Data Protection Impact Assessment (DPIA) under the General Data Protection Regulation (GDPR), the risk assessment process under the Digital Services Act (DSA) and the Conformity Assessment (CA) foreseen under the AI Regulation proposal. In this paper, after briefly introducing the plurality of definitions of fairness in the legal, social and computer science domains, and explaining to which extent the current and upcoming legal framework mandates the adoption of fairness metrics, we will illustrate how AIA could create bridges between all these disciplines, allowing us to build fairer AI solutions. We will then recognise the role of DPIA, DSA risk assessment and CA by discussing the contributions they can offer towards AIA but also identify the aspects lacking therein. We will then identify how these assessment provisions could aid the overall technical discussion of introducing and assessing fairness in AI-based models and processes.

2016

Algorithms, and the data they process, play an increasingly important role in decisions with significant consequences for human welfare. This trend has given rise to calls for greater accountability in algorithm design and implementation, and concern over the emergence of algorithmic discrimination. In that spirit, this paper asks whether and to what extent the European Union’s recently adopted General Data Protection Regulation (GDPR) successfully addresses algorithmic discrimination. As the first piece of legislation to explicitly address algorithmic discrimination, the GDPR sets an important precedent: its success, or failure, will have repercussions that extend well beyond Europe. We argue that while the GDPR’s two primary principles for dealing with algorithmic discrimination—data sanitization and algorithm transparency—are likely inadequate, the legislation also paves the way for third party inspections of algorithms or ‘algorithm audits’. If implemented properly, the algorith...

ArXiv, 2021

Algorithmic decision-making (ADM) increasingly shapes people’s daily lives. Given that such autonomous systems can cause severe harm to individuals and social groups, fairness concerns have arisen. A human-centric approach demanded by scholars and policymakers requires taking people’s fairness perceptions into account when designing and implementing ADM. We provide a comprehensive, systematic literature review synthesizing the existing empirical insights on perceptions of algorithmic fairness from 39 empirical studies spanning multiple domains and scientific disciplines. Through thorough coding, we systemize the current empirical literature along four dimensions: (1) algorithmic predictors, (2) human predictors, (3) comparative effects (human decision-making vs. algorithmic decision-making), and (4) consequences of ADM. While we identify much heterogeneity around the theoretical concepts and empirical measurements of algorithmic fairness, the insights come almost exclusively from We...

Common Market Law Review

Empirical evidence is mounting that artificial intelligence applications driven by machine learning threaten to discriminate against legally protected groups. As ever more decisions are subjected to algorithmic processes, discrimination by algorithms is rightly recognized by policymakers around the world as a key challenge for contemporary societies. This article suggests that algorithmic bias raises intricate questions for EU law. The existing categories of EU anti-discrimination law do not provide an easy fit for algorithmic decision making, and the statistical basis of machine learning generally offers companies a fast-track to justification. Furthermore, victims won't be able to prove their case without access to the data and the algorithms, which they generally lack. To remedy these problems, this article suggests an integrated vision of anti-discrimination and data protection law to enforce fairness in the digital age. More precisely, it shows how the concepts of anti-discrimination law may be combined with the enforcement tools of the GDPR to unlock the algorithmic black box. In doing so, the law should harness a growing literature in computer science on algorithmic fairness that seeks to ensure equal protection at the data and code level. The interplay of anti-discrimination law, data protection law and algorithmic fairness therefore facilitates "equal protection by design". In the end, however, recourse to technology does not prevent the law from making hard normative choices about the implementation of formal or substantive concepts of equality. Understood in this way, the deployment of artificial intelligence not only raises novel risks, but also harbors novel opportunities for consciously designing fair market exchange.

arXiv (Cornell University), 2023

Law and Business, 2021

This paper argues that if we want a sustainable environment of desirable AI systems, we should aim not only at transparent, explainable, fair, lawful, and accountable algorithms, but we also should seek for “just” algorithms, that is, automated decision-making systems that include all the above-mentioned qualities (transparency, explainability, fairness, lawfulness, and accountability). This is possible through a practical “justification” statement and process (eventually derived from algorithmic impact assessment) through which the data controller proves, in practical ways, why the AI system is not unfair, not discriminatory, not obscure, not unlawful, etc. In other words, this justification (eventually derived from data protection impact assessment on the AI system) proves the legality of the system with respect to all data protection principles (fairness, lawfulness, transparency, purpose limitation, data minimisation, accuracy, storage limitation, integrity, and accountability)....