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The topic of algorithmic fairness is of increasing importance to the Human–Computer Interaction research community following accumulating concerns regarding the use and deployment of Artificial Intelligence-based systems. How we conduct research on algorithmic fairness directly influences our inferences and conclusions regarding algorithmic fairness. To better understand the methodological decisions of studies focused on people's perceptions of algorithmic fairness, we systematic analysed relevant papers from the CHI and FAccT conferences. We identified 200 relevant papers published between 1993 and 2022 and assessed their study design, participant sample, and geographical location of participants and authors. Our results highlight that studies are predominantly cross-sectional, cover a wide range of participant roles, and that both authors and participants are primarily from the United States. Based on these findings, we reflect on the potential pitfalls and shortcomings in how the community studies algorithmic fairness. • Mapping of CHI and FAccT papers on algorithmic fairness published 1993–2022. • Over one-third of papers miss details on participant locality and compensation. • Algorithmic fairness research largely restricted to US and other Western countries. • Limited cross-country collaboration, most study samples from a single country. • Remote studies are most common, longitudinal studies are relatively rare. [ABSTRACT FROM AUTHOR]

Many modern machine learning algorithms mitigate bias by enforcing fairness constraints across coarsely-defined groups related to a sensitive attribute like gender or race. However, these algorithms seldom account for within-group heterogeneity and biases that may disproportionately affect some members of a group. In this work, we characterize Social Norm Bias (SNoB), a subtle but consequential type of algorithmic discrimination that may be exhibited by machine learning models, even when these systems achieve group fairness objectives. We study this issue through the lens of gender bias in occupation classification. We quantify SNoB by measuring how an algorithm's predictions are associated with conformity to inferred gender norms. When predicting if an individual belongs to a male-dominated occupation, this framework reveals that "fair" classifiers still favor biographies written in ways that align with inferred masculine norms. We compare SNoB across algorithmic fairness techniques and show that it is frequently a residual bias, and post-processing approaches do not mitigate this type of bias at all. [ABSTRACT FROM AUTHOR]

• We study fairness of AI algorithms in Privileged Group Selection Bias data settings. • A typical domain which often presents such selection bias is AI-based hiring. • We demonstrate that such selection bias can indeed lead to a high algorithmic bias. • We propose three in-process and pre-process fairness mechanisms. • Our methods improve fairness considerably with a minimal compromise in accuracy. An increasing number of decisions regarding the daily lives of human beings are being controlled by artificial intelligence (AI) algorithms. 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. In this paper, we study the fairness of AI algorithms in data settings for which unprivileged groups are extremely under-represented compared to privileged groups. A typical domain which often presents such Privileged Group Selection Bias (PGSB) is AI-based hiring, which stems from an inherent lack of labeled information for rejected applicants. We first demonstrate that such a selection bias can lead to a high algorithmic bias, even if privileged and unprivileged groups are treated exactly the same. We then propose several methods to overcome this type of bias. In particular, we suggest three in-process and pre-process fairness mechanisms, combined with both supervised and semi-supervised learning algorithms. An extensive evaluation that was conducted using two real-world datasets, reveals that the proposed methods are able to improve fairness considerably, with only a minimal compromise in accuracy. This is despite the limited information available for unprivileged groups and the inherent trade-off between fairness and accuracy. [ABSTRACT FROM AUTHOR]