Diversity is often announced as a solution to ethical problems in artificial intelligence (AI), but what exactly is meant by diversity and how it can solve those problems is seldom spelled out. This lack of clarity is one hurdle to motivating diversity in AI. Another hurdle is that while the most common perceptions about what diversity is are too weak to do the work set out for them, stronger notions of diversity are often defended on normative grounds that fail to connect to the values that are important to decision-makers in AI. However, there is a long history of research in feminist philosophy of science and a recent body of work in social epistemology that taken together provide the foundation for a notion of diversity that is both strong enough to do the work demanded of it, and can be defended on epistemic grounds that connect with the values that are important to decision-makers in AI. We clarify and defend that notion here by introducing emergent expertise as a network phenomenon wherein groups of workers with expertise of different types can gain knowledge not available to any individual alone, as long as they have ways of communicating across types of expertise. We illustrate the connected epistemic and ethical benefits of designing technology with diverse groups of workers using the examples of an infamous racist soap dispenser, and the millimeter wave scanners used in US airport security.

中文翻译：

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对算法的感受：多样性、专业性、人工智能

多样性经常被宣布为人工智能（AI）伦理问题的解决方案，但多样性的确切含义以及如何解决这些问题却很少被阐明。这种缺乏清晰度是推动人工智能多样性的障碍之一。另一个障碍是，虽然关于什么是多样性的最常见认知太弱，无法完成为他们设定的工作，但更强烈的多样性概念往往是基于规范性基础来捍卫的，而这些规范性基础无法与对决策者重要的价值观联系起来。人工智能。然而，女权主义科学哲学的研究有着悠久的历史，并且最近在社会认识论方面的研究工作也为多样性概念奠定了基础，这种概念既足够强大，足以完成所要求的工作，又可以其辩护依据与人工智能决策者重要的价值观相关。我们在这里通过将新兴专业知识引入为一种网络现象来澄清和捍卫这一概念，其中具有不同类型专业知识的工人群体可以获得任何个人无法单独获得的知识，只要他们有跨类型专业知识进行交流的方式。我们以臭名昭著的种族主义皂液器和美国机场安检中使用的毫米波扫描仪为例，说明了设计技术与不同群体的工人之间的相互关联的认知和道德效益。