Open AccessPosted Content
Discrimination in the Age of Algorithms
TL;DR: The use of algorithms can make it possible to more easily examine and interrogate the entire decision process, thereby making it far easier to know whether discrimination has occurred as mentioned in this paper, which can also highlight, and make transparent, central tradeoffs among competing values.
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Abstract: The law forbids discrimination. But the ambiguity of human decision-making often makes it extraordinarily hard for the legal system to know whether anyone has actually discriminated. To understand how algorithms affect discrimination, we must therefore also understand how they affect the problem of detecting discrimination. By one measure, algorithms are fundamentally opaque, not just cognitively but even mathematically. Yet for the task of proving discrimination, processes involving algorithms can provide crucial forms of transparency that are otherwise unavailable. These benefits do not happen automatically. But with appropriate requirements in place, the use of algorithms will make it possible to more easily examine and interrogate the entire decision process, thereby making it far easier to know whether discrimination has occurred. By forcing a new level of specificity, the use of algorithms also highlights, and makes transparent, central tradeoffs among competing values. Algorithms are not only a threat to be regulated; with the right safeguards in place, they have the potential to be a positive force for equity.
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Citations
Ensuring machine learning for healthcare works for all.
Liam G. McCoy,John Banja,Marzyeh Ghassemi,Leo Anthony Celi +3 more
- 01 Nov 2020
TL;DR: Machine learning, data science and artificial intelligence technology in healthcare (herein collectively referred to as machine learning for healthcare (MLHC) is positioned to have substantial positive impacts on healthcare.
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- 20 Jun 2022
TL;DR: It is argued that the limitations and risks of current systems cannot be addressed through minor adjustments but require a more fundamental change to the role of PES.
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TL;DR: In this article, the authors examine the growing use of alternative data and machine learning to assess consumer creditworthiness and the implications of this trend for the regulation of consumer credit markets in the UK.
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Selection Problems in the Presence of Implicit Bias
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TL;DR: In this paper, the authors propose a theoretical model for studying the effects of implicit bias on selection decisions, and a way of analyzing possible procedural remedies for implicit bias within this model, where a canonical situation represented by their model is a hiring setting: a recruiting committee is trying to choose a set of finalists to interview among the applicants for a job, evaluating these applicants based on their future potential, but their estimates of potential are skewed by implicit bias against members of one group.
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