算法偏见:通过三维可靠人工智能框架整合社会科学研究

IF 6.3 2区 心理学 Q1 PSYCHOLOGY, MULTIDISCIPLINARY Current Opinion in Psychology Pub Date : 2024-07-01 DOI:10.1016/j.copsyc.2024.101836
Kalinda Ukanwa
{"title":"算法偏见:通过三维可靠人工智能框架整合社会科学研究","authors":"Kalinda Ukanwa","doi":"10.1016/j.copsyc.2024.101836","DOIUrl":null,"url":null,"abstract":"<div><p>Algorithmic bias has emerged as a critical challenge in the age of responsible production of artificial intelligence (AI). This paper reviews recent research on algorithmic bias and proposes increased engagement of psychological and social science research to understand antecedents and consequences of algorithmic bias. Through the lens of the 3-D Dependable AI Framework, this article explores how social science disciplines, such as psychology, can contribute to identifying and mitigating bias at the Design, Develop, and Deploy stages of the AI life cycle. Finally, we propose future research directions to further address the complexities of algorithmic bias and its societal implications.</p></div>","PeriodicalId":48279,"journal":{"name":"Current Opinion in Psychology","volume":"58 ","pages":"Article 101836"},"PeriodicalIF":6.3000,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2352250X24000496/pdfft?md5=db9280dadbd4af45a3b4670a405a5422&pid=1-s2.0-S2352250X24000496-main.pdf","citationCount":"0","resultStr":"{\"title\":\"Algorithmic bias: Social science research integration through the 3-D Dependable AI Framework\",\"authors\":\"Kalinda Ukanwa\",\"doi\":\"10.1016/j.copsyc.2024.101836\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Algorithmic bias has emerged as a critical challenge in the age of responsible production of artificial intelligence (AI). This paper reviews recent research on algorithmic bias and proposes increased engagement of psychological and social science research to understand antecedents and consequences of algorithmic bias. Through the lens of the 3-D Dependable AI Framework, this article explores how social science disciplines, such as psychology, can contribute to identifying and mitigating bias at the Design, Develop, and Deploy stages of the AI life cycle. Finally, we propose future research directions to further address the complexities of algorithmic bias and its societal implications.</p></div>\",\"PeriodicalId\":48279,\"journal\":{\"name\":\"Current Opinion in Psychology\",\"volume\":\"58 \",\"pages\":\"Article 101836\"},\"PeriodicalIF\":6.3000,\"publicationDate\":\"2024-07-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S2352250X24000496/pdfft?md5=db9280dadbd4af45a3b4670a405a5422&pid=1-s2.0-S2352250X24000496-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Current Opinion in Psychology\",\"FirstCategoryId\":\"102\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2352250X24000496\",\"RegionNum\":2,\"RegionCategory\":\"心理学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"PSYCHOLOGY, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Current Opinion in Psychology","FirstCategoryId":"102","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2352250X24000496","RegionNum":2,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"PSYCHOLOGY, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0

摘要

在负责任地生产人工智能(AI)的时代,算法偏见已成为一项严峻挑战。本文回顾了有关算法偏见的最新研究,并建议加强心理学和社会科学研究的参与,以了解算法偏见的前因后果。通过 3-D 可依赖人工智能框架的视角,本文探讨了心理学等社会科学学科如何在人工智能生命周期的设计、开发和部署阶段为识别和减少偏见做出贡献。最后,我们提出了未来的研究方向,以进一步解决算法偏见的复杂性及其社会影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Algorithmic bias: Social science research integration through the 3-D Dependable AI Framework

Algorithmic bias has emerged as a critical challenge in the age of responsible production of artificial intelligence (AI). This paper reviews recent research on algorithmic bias and proposes increased engagement of psychological and social science research to understand antecedents and consequences of algorithmic bias. Through the lens of the 3-D Dependable AI Framework, this article explores how social science disciplines, such as psychology, can contribute to identifying and mitigating bias at the Design, Develop, and Deploy stages of the AI life cycle. Finally, we propose future research directions to further address the complexities of algorithmic bias and its societal implications.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Current Opinion in Psychology
Current Opinion in Psychology PSYCHOLOGY, MULTIDISCIPLINARY-
CiteScore
12.10
自引率
3.40%
发文量
293
审稿时长
53 days
期刊介绍: Current Opinion in Psychology is part of the Current Opinion and Research (CO+RE) suite of journals and is a companion to the primary research, open access journal, Current Research in Ecological and Social Psychology. CO+RE journals leverage the Current Opinion legacy of editorial excellence, high-impact, and global reach to ensure they are a widely-read resource that is integral to scientists' workflows. Current Opinion in Psychology is divided into themed sections, some of which may be reviewed on an annual basis if appropriate. The amount of space devoted to each section is related to its importance. The topics covered will include: * Biological psychology * Clinical psychology * Cognitive psychology * Community psychology * Comparative psychology * Developmental psychology * Educational psychology * Environmental psychology * Evolutionary psychology * Health psychology * Neuropsychology * Personality psychology * Social psychology
期刊最新文献
From primary to pluralistic: A typology of intersectionality Evaluative conditioning as a source gut feelings and its potential for behavioral nudging Diversity initiatives: Intended and unintended effects A sender-message-receiver (SMeR) framework for communicating persuasive social norms – The case of climate change mitigation behavioral change Hype-free AI: How AI actually impacts psychology in research, the workplace, the marketplace, and beyond
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1