The Ugly Truth About Ourselves and Our Robot Creations: The Problem of Bias and Social Inequity
The Ugly Truth About Ourselves and Our Robot Creations: The Problem of Bias and Social Inequity
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DOI：
10.1007/s11948-017-9975-2
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发表时间：
2018-10-01
影响因子：
3.7
通讯作者：
Borenstein, Jason
中科院分区：
文献类型：
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作者：
Howard, Ayanna;Borenstein, Jason
最近，人们对偏见及其对专门人工智能（AI）应用的影响的关注激增。种族主义和性别歧视的指控已经渗透到对话中，因为搜索引擎向男性而非女性提供高薪技术工作的招聘信息，或者在输入黑人青少年等关键词时提供逮捕照片。学习算法正在不断发展；它们通常是通过解析大型在线信息数据集而创建的，同时具有众包群众赋予的真实标签。这些专门的人工智能算法已经从研究人员和初创公司的头脑中解放出来，并向公众发布。然而，尽管这些算法可能很聪明，但它们仍然保留着一些渗透到社会中的相同偏见。他们在数据集中发现了反映隐性偏见的模式，并以此强调和强化这些偏见作为全球真理。本文描述了偏见如何融入当前人工智能和机器人系统的具体例子，以及它如何影响此类系统的未来设计。更具体地说，我们提请注意偏见如何影响（1）机器人维和人员、（2）自动驾驶汽车和（3）医疗机器人的功能。最后，我们概述了可以采取的措施，以减轻或阻止渗透机器人技术带来的偏见。
Recently, there has been an upsurge of attention focused on bias and its impact on specialized artificial intelligence (AI) applications. Allegations of racism and sexism have permeated the conversation as stories surface about search engines delivering job postings for well-paying technical jobs to men and not women, or providing arrest mugshots when keywords such as black teenagers are entered. Learning algorithms are evolving; they are often created from parsing through large datasets of online information while having truth labels bestowed on them by crowd-sourced masses. These specialized AI algorithms have been liberated from the minds of researchers and startups, and released onto the public. Yet intelligent though they may be, these algorithms maintain some of the same biases that permeate society. They find patterns within datasets that reflect implicit biases and, in so doing, emphasize and reinforce these biases as global truth. This paper describes specific examples of how bias has infused itself into current AI and robotic systems, and how it may affect the future design of such systems. More specifically, we draw attention to how bias may affect the functioning of (1) a robot peacekeeper, (2) a self-driving car, and (3) a medical robot. We conclude with an overview of measures that could be taken to mitigate or halt bias from permeating robotic technology.