The Algorithms
The Algorithms
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算法
DOI：
10.1007/978-3-030-78401-0_7
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发表时间：
2021
期刊：
影响因子：
--
通讯作者：
Rafael Priego
中科院分区：
文献类型：
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作者：
Eloy Irigoyen;Ekaitz Larzabal;Rafael Priego
人工智能 (AI) 工具的不断部署为医疗保健、就业、社会福利和政府政策等不同领域的决策提供信息，这给残疾人带来了严重的风险，事实证明，他们在人工智能实施中面临着偏见。尽管在分析和减轻算法偏差方面已经开展了大量工作，但人工智能应用中出现偏差的更广泛机制尚不清楚，这阻碍了从根源上解决偏差的努力。在本文中，我们说明了人工智能辅助决策中的偏差是如何由一系列具体的设计决策产生的，当单独考虑时，每一个设计决策似乎都是独立且无偏见的。这些设计决策包括基本问题的表述、选择用于分析的数据、人工智能技术的使用以及除核心算法设计之外的操作设计元素。我们利用不同决策环境中常见的三种残疾历史模型来展示残疾定义的差异如何导致在设计的每个方面产生高度不同的决策，从而导致人工智能技术具有各种偏见和下游影响。我们进一步表明，由于整个人工智能设计过程中缺乏透明度和残疾人参与，基础设计阶段对残疾的不恰当定义所产生的潜在危害会进一步放大。我们的分析提供了一个框架，用于在决策环境中批判性地检查人工智能技术，并指导残疾相关人工智能分析设计实践的开发。我们提出这篇文章是为了提供关键问题，以促进以残疾为主导的设计和参与式开发，从而在与残疾相关的环境中产生更公平和公正的人工智能技术。
The increasing deployment of artificial intelligence (AI) tools to inform decision-making across diverse areas including healthcare, employment, social benefits, and government policy, presents a serious risk for disabled people, who have been shown to face bias in AI implementations. While there has been significant work on analysing and mitigating algorithmic bias, the broader mechanisms of how bias emerges in AI applications are not well understood, hampering efforts to address bias where it begins. In this article, we illustrate how bias in AI-assisted decision-making can arise from a range of specific design decisions, each of which may seem self-contained and non-biasing when considered separately. These design decisions include basic problem formulation, the data chosen for analysis, the use the AI technology is put to, and operational design elements in addition to the core algorithmic design. We draw on three historical models of disability common to different decision-making settings to demonstrate how differences in the definition of disability can lead to highly distinct decisions on each of these aspects of design, leading in turn to AI technologies with a variety of biases and downstream effects. We further show that the potential harms arising from inappropriate definitions of disability in fundamental design stages are further amplified by a lack of transparency and disabled participation throughout the AI design process. Our analysis provides a framework for critically examining AI technologies in decision-making contexts and guiding the development of a design praxis for disability-related AI analytics. We put forth this article to provide key questions to facilitate disability-led design and participatory development to produce more fair and equitable AI technologies in disability-related contexts.