Rafael Lucas Borba , Iuri Emmanuel de Paula Ferreira , Paulo Henrique Bertucci Ramos
{"title":"解决公司运营的人工智能系统中的歧视性偏见:对最终用户观点的分析","authors":"Rafael Lucas Borba , Iuri Emmanuel de Paula Ferreira , Paulo Henrique Bertucci Ramos","doi":"10.1016/j.technovation.2024.103118","DOIUrl":null,"url":null,"abstract":"<div><div>The use of AI in different applications for different purposes has raised concerns due to discriminatory biases that have been identified in the technology. This paper aims to identify and analyze some of the main measures proposed by Bill No. 2338/23 of the Federative Republic of Brazil to combat discriminatory bias that companies should adopt to provide and/or operate fair and non-discriminatory AIs. To do so, it will first attempt to measure and analyze people's perceptions of the possibility that AI systems are discriminatory. For this a qualitative descriptive exploratory was made using as a reference sample the inhabitants of the Southeast region of Brasil. The survey results suggest that people are more aware that AIs are not neutral and that they may come to incorporate and reproduce prejudices and discriminations present in society. The incorporation of such biases is the result of issues related to the quality and diversity of the data used, inaccuracies in the algorithms employed, and biases on the part of both developers and operators. Thus, this work sought to reduce this gap and at the same time break the barrier of the lack of dialogue with the public in order to contribute to a democratic debate with society.</div></div>","PeriodicalId":49444,"journal":{"name":"Technovation","volume":"138 ","pages":"Article 103118"},"PeriodicalIF":11.1000,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Addressing discriminatory bias in artificial intelligence systems operated by companies: An analysis of end-user perspectives\",\"authors\":\"Rafael Lucas Borba , Iuri Emmanuel de Paula Ferreira , Paulo Henrique Bertucci Ramos\",\"doi\":\"10.1016/j.technovation.2024.103118\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>The use of AI in different applications for different purposes has raised concerns due to discriminatory biases that have been identified in the technology. This paper aims to identify and analyze some of the main measures proposed by Bill No. 2338/23 of the Federative Republic of Brazil to combat discriminatory bias that companies should adopt to provide and/or operate fair and non-discriminatory AIs. To do so, it will first attempt to measure and analyze people's perceptions of the possibility that AI systems are discriminatory. For this a qualitative descriptive exploratory was made using as a reference sample the inhabitants of the Southeast region of Brasil. The survey results suggest that people are more aware that AIs are not neutral and that they may come to incorporate and reproduce prejudices and discriminations present in society. The incorporation of such biases is the result of issues related to the quality and diversity of the data used, inaccuracies in the algorithms employed, and biases on the part of both developers and operators. Thus, this work sought to reduce this gap and at the same time break the barrier of the lack of dialogue with the public in order to contribute to a democratic debate with society.</div></div>\",\"PeriodicalId\":49444,\"journal\":{\"name\":\"Technovation\",\"volume\":\"138 \",\"pages\":\"Article 103118\"},\"PeriodicalIF\":11.1000,\"publicationDate\":\"2024-10-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Technovation\",\"FirstCategoryId\":\"91\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0166497224001688\",\"RegionNum\":1,\"RegionCategory\":\"管理学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, INDUSTRIAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Technovation","FirstCategoryId":"91","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0166497224001688","RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, INDUSTRIAL","Score":null,"Total":0}
Addressing discriminatory bias in artificial intelligence systems operated by companies: An analysis of end-user perspectives
The use of AI in different applications for different purposes has raised concerns due to discriminatory biases that have been identified in the technology. This paper aims to identify and analyze some of the main measures proposed by Bill No. 2338/23 of the Federative Republic of Brazil to combat discriminatory bias that companies should adopt to provide and/or operate fair and non-discriminatory AIs. To do so, it will first attempt to measure and analyze people's perceptions of the possibility that AI systems are discriminatory. For this a qualitative descriptive exploratory was made using as a reference sample the inhabitants of the Southeast region of Brasil. The survey results suggest that people are more aware that AIs are not neutral and that they may come to incorporate and reproduce prejudices and discriminations present in society. The incorporation of such biases is the result of issues related to the quality and diversity of the data used, inaccuracies in the algorithms employed, and biases on the part of both developers and operators. Thus, this work sought to reduce this gap and at the same time break the barrier of the lack of dialogue with the public in order to contribute to a democratic debate with society.
期刊介绍:
The interdisciplinary journal Technovation covers various aspects of technological innovation, exploring processes, products, and social impacts. It examines innovation in both process and product realms, including social innovations like regulatory frameworks and non-economic benefits. Topics range from emerging trends and capital for development to managing technology-intensive ventures and innovation in organizations of different sizes. It also discusses organizational structures, investment strategies for science and technology enterprises, and the roles of technological innovators. Additionally, it addresses technology transfer between developing countries and innovation across enterprise, political, and economic systems.