检查AI文本生成器中的宗教偏见

D. Muralidhar
{"title":"检查AI文本生成器中的宗教偏见","authors":"D. Muralidhar","doi":"10.1145/3461702.3462469","DOIUrl":null,"url":null,"abstract":"One of the biggest reasons artificial intelligence (AI) gets a backlash is because of inherent biases in AI software. Deep learning algorithms use data fed into the systems to find patterns to draw conclusions used to make application decisions. Patterns in data fed into machine learning algorithms have revealed that the AI software decisions have biases embedded within them. Algorithmic audits can certify that the software is making responsible decisions. These audits verify the standards centered around the various AI principles such as explainability, accountability, human-centered values, such as, fairness and transparency, to increase the trust in the algorithm and the software systems that implement AI algorithms.","PeriodicalId":197336,"journal":{"name":"Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society","volume":"19 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Examining Religion Bias in AI Text Generators\",\"authors\":\"D. Muralidhar\",\"doi\":\"10.1145/3461702.3462469\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"One of the biggest reasons artificial intelligence (AI) gets a backlash is because of inherent biases in AI software. Deep learning algorithms use data fed into the systems to find patterns to draw conclusions used to make application decisions. Patterns in data fed into machine learning algorithms have revealed that the AI software decisions have biases embedded within them. Algorithmic audits can certify that the software is making responsible decisions. These audits verify the standards centered around the various AI principles such as explainability, accountability, human-centered values, such as, fairness and transparency, to increase the trust in the algorithm and the software systems that implement AI algorithms.\",\"PeriodicalId\":197336,\"journal\":{\"name\":\"Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society\",\"volume\":\"19 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-07-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3461702.3462469\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3461702.3462469","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4

摘要

人工智能(AI)遭到抵制的最大原因之一是人工智能软件固有的偏见。深度学习算法使用输入系统的数据来查找模式,从而得出用于做出应用程序决策的结论。输入机器学习算法的数据模式表明,人工智能软件的决策中嵌入了偏见。算法审计可以证明软件正在做出负责任的决定。这些审计验证了以各种人工智能原则为中心的标准,如可解释性、问责制、以人为本的价值观,如公平和透明度,以增加对算法和实施人工智能算法的软件系统的信任。
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
Examining Religion Bias in AI Text Generators
One of the biggest reasons artificial intelligence (AI) gets a backlash is because of inherent biases in AI software. Deep learning algorithms use data fed into the systems to find patterns to draw conclusions used to make application decisions. Patterns in data fed into machine learning algorithms have revealed that the AI software decisions have biases embedded within them. Algorithmic audits can certify that the software is making responsible decisions. These audits verify the standards centered around the various AI principles such as explainability, accountability, human-centered values, such as, fairness and transparency, to increase the trust in the algorithm and the software systems that implement AI algorithms.
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
自引率
0.00%
发文量
0
期刊最新文献
Beyond Reasonable Doubt: Improving Fairness in Budget-Constrained Decision Making using Confidence Thresholds Measuring Automated Influence: Between Empirical Evidence and Ethical Values Artificial Intelligence and the Purpose of Social Systems Ethically Compliant Planning within Moral Communities Co-design and Ethical Artificial Intelligence for Health: Myths and Misconceptions
×
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