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Dec 6, 2019 · Congress is also considering legislation to mandate company consideration of algorithmic fairness. The Algorithmic Accountability Act of 2019 requires companies to assess their automatic decision ...
Jan 14, 2022 · Appropriate factors used in the decision-making does not ensure perceived fairness. •. Trust to the system is strongly affected by the user's perception of fairness. •. Algorithmic fairness is defined as the use of objective factors. •. Sensitive attributes can be the most likely cause of unfairness.
- Styliani Kleanthous, Maria Kasinidou, Pınar Barlas, Jahna Otterbacher
- 2021
A recent wave of research has attempted to define fairness quantitatively. In particular, this work has explored what fairness might mean in the context of decisions based on the predictions of statistical and machine learning models. The rapid growth of this new field has led to wildly inconsistent motivations, terminology, and notation, presenting a serious challenge for cataloging and ...
Nov 12, 2023 · Empathy-Fueled Fairness in Decision-Making. Fairness is the principle of treating everyone justly and equitably. In leadership, fair decision-making is crucial to maintaining trust, transparency, and cohesion within a team. When leaders make decisions with empathy, they consider the impact on individuals and avoid favoritism or bias.
Oct 10, 2022 · Studies that tapped into people's general notions of algorithmic fairness (Dodge et al., 2019; Shin and Park, 2019) yielded mixed results: some respondents perceived “the very idea of an algorithmic system making an important decision on the basis of past data […] unfair” (Binns et al., 2018: 9), while other participants argued that algorithms are by definition impartial (Lee and Rich ...
Initially, we will focus on concepts of fairness, welfare, and justice in the context of allocative decision making by a state or organization. This entry point is reasonable, as. algorithmic decision making systems usually make decisions for others, and. discussion of allocative decisions are plentiful, accessible, and important.
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Introduction — Fairness & Algorithmic Decision Making. 1. Introduction. This course broadly approaches the issue of fairness in algorithmic decision making for the practicing data scientists. We emphasize reasoning about data-driven (algorithmic) decision making and its interaction with society. This ability to reason helps the data science ...
