Moore Jared
Wadhwani Institute for Artificial Intelligence, Mumbai, India.
Front Big Data. 2019 Sep 11;2:32. doi: 10.3389/fdata.2019.00032. eCollection 2019.
Hype surrounds the promotions, aspirations, and notions of "artificial intelligence (AI) for social good" and its related permutations. These terms, as used in data science and particularly in public discourse, are vague. Far from being irrelevant to data scientists or practitioners of AI, the terms create the public notion of the systems built. Through a critical reflection, I explore how notions of AI for social good are vague, offer insufficient criteria for judgement, and elide the externalities and structural interdependence of AI systems. Instead, the field known as "AI for social good" is best understood and referred to as "AI for not bad."
围绕“造福社会的人工智能（AI）”的宣传、抱负和理念及其相关变体充斥着炒作。这些术语在数据科学中，尤其是在公众话语中使用时，含义模糊。这些术语远非与数据科学家或人工智能从业者无关，它们塑造了公众对所构建系统的认知。通过批判性反思，我探讨了造福社会的人工智能理念是如何模糊不清、提供的判断标准不足，以及如何忽略了人工智能系统的外部性和结构相互依存性。相反，“造福社会的人工智能”这一领域最好被理解并称为“差强人意的人工智能”。
