558,854 research outputs found

    AS-672-08 Resolution on Revisions to Fairness Board Description and Procedures

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    Changes made to the Fairness Board Description and Procedures noting reporting changes made in cases of cheating and plagiarism

    AS-800-15 Resolution on Modification of Retention of Exams Policy

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    50 Years of Test (Un)fairness: Lessons for Machine Learning

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    Quantitative definitions of what is unfair and what is fair have been introduced in multiple disciplines for well over 50 years, including in education, hiring, and machine learning. We trace how the notion of fairness has been defined within the testing communities of education and hiring over the past half century, exploring the cultural and social context in which different fairness definitions have emerged. In some cases, earlier definitions of fairness are similar or identical to definitions of fairness in current machine learning research, and foreshadow current formal work. In other cases, insights into what fairness means and how to measure it have largely gone overlooked. We compare past and current notions of fairness along several dimensions, including the fairness criteria, the focus of the criteria (e.g., a test, a model, or its use), the relationship of fairness to individuals, groups, and subgroups, and the mathematical method for measuring fairness (e.g., classification, regression). This work points the way towards future research and measurement of (un)fairness that builds from our modern understanding of fairness while incorporating insights from the past.Comment: FAT* '19: Conference on Fairness, Accountability, and Transparency (FAT* '19), January 29--31, 2019, Atlanta, GA, US

    Fairness of performance evaluation procedures and job satisfaction: the role of outcome-based and non-outcome based effects

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    Prior management accounting studies on fairness perceptions have overlooked two important issues. First, no prior management accounting studies have investigated how procedural fairness, by itself, affects managers' job satisfaction. Second, management accounting researchers have not demonstrated how conflicting theories on procedural fairness can be integrated and explained in a coherent manner. Our model proposes that fairness of procedures for performance evaluation affects job satisfaction through two distinct processes. The first is out-come-based through fairness of outcomes (distributive fairness). The second is non-outcome-based through trust in superior and organisational commitment. Based on a sample of 110 managers, the results indicate that while procedural fairness perceptions affect job satisfaction through both processes, the non-outcome-based process is much stronger than the outcome-based process. These results may be used to develop a unified theory on procedural fairness effects
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