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Fairness perceptions of algorithmic decision-making: A systematic review of the empirical literature
Algorithmic decision-making 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 considering people's fairness perceptions when designing and implementing algorithmic decision-making. We provide a comprehensive, systematic literature review synthesizing the existing empirical insights on perceptions of algorithmic fairness from 58 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 algorithmic decision-making. While we identify much heterogeneity around the theoretical concepts and empirical measurements of algorithmic fairness, the insights come almost exclusively from Western-democratic contexts. By advocating for more interdisciplinary research adopting a society-in-the-loop framework, we hope our work will contribute to fairer and more responsible algorithmic decision-making.
Journal Article
Do the Ends Justify the Means? Variation in the Distributive and Procedural Fairness of Machine Learning Algorithms
Recent advances in machine learning methods have created opportunities to eliminate unfairness from algorithmic decision making. Multiple computational techniques (i.e., algorithmic fairness criteria) have arisen out of this work. Yet, urgent questions remain about the perceived fairness of these criteria and in which situations organizations should use them. In this paper, we seek to gain insight into these questions by exploring fairness perceptions of five algorithmic criteria. We focus on two key dimensions of fairness evaluations: distributive fairness and procedural fairness. We shed light on variation in the potential for different algorithmic criteria to facilitate distributive fairness. Subsequently, we discuss procedural fairness and provide a framework for understanding how algorithmic criteria relate to essential aspects of this construct, which helps to identify when a specific criterion is suitable. From a practical standpoint, we encourage organizations to recognize that managing fairness in machine learning systems is complex, and that adopting a blind or one-size-fits-all mentality toward algorithmic criteria will surely damage people’s attitudes and trust in automated technology. Instead, firms should carefully consider the subtle yet significant differences between these technical solutions.
Journal Article
A theory of fairness and social welfare
\"The definition and measurement of social welfare have been a vexed issue for the past century. This book makes a constructive, easily applicable proposal and suggests how to evaluate the economic situation of a society in a way that gives priority to the worse-off and that respects each individual's preferences over his or her own consumption, work, leisure and so on. This approach resonates with the current concern to go 'beyond the GDP' in the measurement of social progress. Compared to technical studies in welfare economics, this book emphasizes constructive results rather than paradoxes and impossibilities, and shows how one can start from basic principles of efficiency and fairness and end up with concrete evaluations of policies. Compared to more philosophical treatments of social justice, this book is more precise about the definition of social welfare and reaches conclusions about concrete policies and institutions only after a rigorous derivation from clearly stated principles\"-- Provided by publisher.
Book
Managing contracts for fairness in buyer-supplier exchanges
Despite the centrality of fairness in the moral and social fabric of governance, few studies relate fairness to contracting research. This paper assesses whether fairness accounts for the effects of contractual complexity and contractual recurrence on exchange performance. Based on a sample of 283 buyer—supplier dyads, we find that procedural fairness partially mediates the effect of contractual complexity, whereas distributive fairness partially mediates the effect of contractual recurrence in fostering exchange performance. Moreover, monitoring better supports the use of contractual complexity, whereas socializing better supports the use of contractual recurrence in enhancing fairness perceptions. These results suggest that contractual design must go beyond its safeguarding function to establish a fair frame of reference, and managers should complement contracts with appropriate practices (e.g., monitoring or socializing).
Journal Article
Algorithmic fairness datasets: the story so far
Data-driven algorithms are studied and deployed in diverse domains to support critical decisions, directly impacting people’s well-being. As a result, a growing community of researchers has been investigating the equity of existing algorithms and proposing novel ones, advancing the understanding of risks and opportunities of automated decision-making for historically disadvantaged populations. Progress in fair machine learning and equitable algorithm design hinges on data, which can be appropriately used only if adequately documented. Unfortunately, the algorithmic fairness community, as a whole, suffers from a collective data documentation debt caused by a lack of information on specific resources (opacity) and scatteredness of available information (sparsity). In this work, we target this data documentation debt by surveying over two hundred datasets employed in algorithmic fairness research, and producing standardized and searchable documentation for each of them. Moreover we rigorously identify the three most popular fairness datasets, namely Adult, COMPAS, and German Credit, for which we compile in-depth documentation. This unifying documentation effort supports multiple contributions. Firstly, we summarize the merits and limitations of Adult, COMPAS, and German Credit, adding to and unifying recent scholarship, calling into question their suitability as general-purpose fairness benchmarks. Secondly, we document hundreds of available alternatives, annotating their domain and supported fairness tasks, along with additional properties of interest for fairness practitioners and researchers, including their format, cardinality, and the sensitive attributes they encode. We summarize this information, zooming in on the tasks, domains, and roles of these resources. Finally, we analyze these datasets from the perspective of five important data curation topics: anonymization, consent, inclusivity, labeling of sensitive attributes, and transparency. We discuss different approaches and levels of attention to these topics, making them tangible, and distill them into a set of best practices for the curation of novel resources.
Journal Article
