Ethics Sheet for Automatic Emotion Recognition and Sentiment Analysis

IF 3.7 2区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Computational Linguistics Pub Date : 2021-09-17 DOI:10.1162/coli_a_00433
Saif M. Mohammad
{"title":"Ethics Sheet for Automatic Emotion Recognition and Sentiment Analysis","authors":"Saif M. Mohammad","doi":"10.1162/coli_a_00433","DOIUrl":null,"url":null,"abstract":"Abstract The importance and pervasiveness of emotions in our lives makes affective computing a tremendously important and vibrant line of work. Systems for automatic emotion recognition (AER) and sentiment analysis can be facilitators of enormous progress (e.g., in improving public health and commerce) but also enablers of great harm (e.g., for suppressing dissidents and manipulating voters). Thus, it is imperative that the affective computing community actively engage with the ethical ramifications of their creations. In this article, I have synthesized and organized information from AI Ethics and Emotion Recognition literature to present fifty ethical considerations relevant to AER. Notably, this ethics sheet fleshes out assumptions hidden in how AER is commonly framed, and in the choices often made regarding the data, method, and evaluation. Special attention is paid to the implications of AER on privacy and social groups. Along the way, key recommendations are made for responsible AER. The objective of the ethics sheet is to facilitate and encourage more thoughtfulness on why to automate, how to automate, and how to judge success well before the building of AER systems. Additionally, the ethics sheet acts as a useful introductory document on emotion recognition (complementing survey articles).","PeriodicalId":55229,"journal":{"name":"Computational Linguistics","volume":"48 1","pages":"239-278"},"PeriodicalIF":3.7000,"publicationDate":"2021-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"34","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computational Linguistics","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1162/coli_a_00433","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 34

Abstract

Abstract The importance and pervasiveness of emotions in our lives makes affective computing a tremendously important and vibrant line of work. Systems for automatic emotion recognition (AER) and sentiment analysis can be facilitators of enormous progress (e.g., in improving public health and commerce) but also enablers of great harm (e.g., for suppressing dissidents and manipulating voters). Thus, it is imperative that the affective computing community actively engage with the ethical ramifications of their creations. In this article, I have synthesized and organized information from AI Ethics and Emotion Recognition literature to present fifty ethical considerations relevant to AER. Notably, this ethics sheet fleshes out assumptions hidden in how AER is commonly framed, and in the choices often made regarding the data, method, and evaluation. Special attention is paid to the implications of AER on privacy and social groups. Along the way, key recommendations are made for responsible AER. The objective of the ethics sheet is to facilitate and encourage more thoughtfulness on why to automate, how to automate, and how to judge success well before the building of AER systems. Additionally, the ethics sheet acts as a useful introductory document on emotion recognition (complementing survey articles).
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
情绪自动识别与情绪分析伦理表
摘要情感在我们生活中的重要性和普遍性使情感计算成为一项极其重要和充满活力的工作。自动情绪识别(AER)和情绪分析系统可能是巨大进步的推动者(例如,在改善公共卫生和商业方面),但也可能造成巨大伤害(例如,镇压持不同政见者和操纵选民)。因此,情感计算社区必须积极参与他们创作的伦理影响。在这篇文章中,我综合并整理了人工智能伦理和情绪识别文献中的信息,提出了与AER相关的50个伦理考虑。值得注意的是,这份道德规范表充实了隐藏在AER通常是如何制定的,以及在数据、方法和评估方面经常做出的选择中的假设。特别关注AER对隐私和社会群体的影响。在此过程中,为负责任的AER提出了关键建议。道德规范表的目的是促进和鼓励在建立AER系统之前就为什么要自动化、如何自动化以及如何判断成功进行更多思考。此外,道德规范表是关于情绪识别的有用介绍性文件(补充调查文章)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
Computational Linguistics
Computational Linguistics 工程技术-计算机:跨学科应用
CiteScore
15.80
自引率
0.00%
发文量
45
审稿时长
>12 weeks
期刊介绍: Computational Linguistics, the longest-running publication dedicated solely to the computational and mathematical aspects of language and the design of natural language processing systems, provides university and industry linguists, computational linguists, AI and machine learning researchers, cognitive scientists, speech specialists, and philosophers with the latest insights into the computational aspects of language research.
期刊最新文献
Generation and Polynomial Parsing of Graph Languages with Non-Structural Reentrancies Languages through the Looking Glass of BPE Compression Capturing Fine-Grained Regional Differences in Language Use through Voting Precinct Embeddings Machine Learning for Ancient Languages: A Survey Statistical Methods for Annotation Analysis by Silviu Paun, Ron Artstein, and Massimo Poesio
×
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