情绪自动识别与情绪分析伦理表

IF 3.7 2区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Saif M. Mohammad
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引用次数: 34

摘要

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。
Ethics Sheet for Automatic Emotion Recognition and Sentiment Analysis
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).
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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.
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