The Ugly Truth About Ourselves and Our Robot Creations: The Problem of Bias and Social Inequity.
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...
| Publicado en: | Science & Engineering Ethics Vol. 24; no. 5; pp. 1521 - 1537 |
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| Autores principales: | , |
| Formato: | Artículo |
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Springer Nature
Oct2018
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=132498931&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 132498931 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 13533452 GNI jtl: Science & Engineering Ethics issn: 13533452 maglogo: N pubinfo: dt: Oct2018 vid: 24 iid: 5 pid: 237 pub: Springer Nature artinfo: ui: 132498931 10.1007/s11948-017-9975-2 ppf: 1521 ppct: 16 formats: fmt: @attributes: type: P size: 468KB tig: atl: The Ugly Truth About Ourselves and Our Robot Creations: The Problem of Bias and Social Inequity. aug: au: Howard, Ayanna Borenstein, Jason affil: School of Electrical & Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA School of Public Policy, Georgia Institute of Technology, 685 Cherry Street, 30332-0345, Atlanta, GA, USA su: Artificial intelligence Decision making Prejudices Information technology Big data sug: subj: Artificial intelligence Decision making Prejudices Information technology Big data keyword: Design ethics Implicit bias Professional ethics Robot ethics ab: 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. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y custom: Science & Engineering Ethics is a copyright of Springer, 2018. All Rights Reserved. item: Science & Engineering Ethics holder: Springer Nature dt: @attributes: year: 2018 holdings: @attributes: islocal: N |
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