.png){kind=icon}
返回
The algorithm audit: Scoring the algorithms that score us
.png){kind=icon}
OA
AI
DOI：10.1177/2053951720983865.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In recent years, the ethical impact of AI has been increasingly scrutinized, with public scandals emerging over biased outcomes, lack of transparency, and the misuse of data. This has led to a growing mistrust of AI and increased calls for mandated ethical audits of algorithms. Current proposals for ethical assessment of algorithms are either too high level to be put into practice without further guidance, or they focus on very specific and technical notions of fairness or transparency that do not consider multiple stakeholders or the broader social context. In this article, we present an auditing framework to guide the ethical assessment of an algorithm. The audit instrument itself is comprised of three elements: a list of possible interests of stakeholders affected by the algorithm, an assessment of metrics that describe key ethically salient features of the algorithm, and a relevancy matrix that connects the assessed metrics to stakeholder interests. The proposed audit instrument yields an ethical evaluation of an algorithm that could be used by regulators and others interested in doing due diligence, while paying careful attention to the complex societal context within which the algorithm is deployed.
Keyword:
Algorithm audits
algorithm ethics
ethics
ethics of AI
machine learning
machine learning and ethics
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
5.9
论文数:
718
被引数:
5.3K
机构
引用论文
分享收藏Dissecting racial bias in an algorithm used to manage the health of populations在用于管理人群健康的算法中剖析种族偏见.png)
SCIENCEOAAI
SCIENCEIF45.8
OAAI分享收藏分享收藏Analysis of MHD and heat transfer effects with variable viscosity through ductus efferentesAIP AdvancesOAAI
AIP AdvancesIF0
OAAI分享收藏
查看更多内容