Susan Gardner Archambault , Shalini Ramachandran , Elisa Acosta , Sheree Fu
{"title":"大学生算法素养的伦理维度:案例研究与跨学科联系","authors":"Susan Gardner Archambault , Shalini Ramachandran , Elisa Acosta , Sheree Fu","doi":"10.1016/j.acalib.2024.102865","DOIUrl":null,"url":null,"abstract":"<div><p>This article addresses three key questions related to the ethical facets of algorithmic literacy. First, it synthesizes existing literature to identify six core ethical components, including bias, privacy, transparency, accountability, accuracy, and non-maleficence. Second, a crosswalk maps the intersections of these principles across the Association of College and Research Libraries' Framework for Information Literacy for Higher Education and the Association of Computing Machinery's Code of Ethics and Professional Conduct and Joint Statement on Principles for Responsible Algorithmic Systems. This analysis reveals significant overlap on issues like unfairness and transparency, helping prioritize topics for instruction. Finally, case studies showcase pedagogical strategies for teaching ethical considerations, informed by the crosswalk. Workshops for diverse undergraduates and computer science students employed reallife instances of algorithmic bias to prompt reflection on unintended harm, contestability, and responsible development. Pre-post surveys indicated expanded critical perspectives after the interventions. By systematically examining shared values and testing instructional approaches, this study provides practical tools to shape ethical thinking on algorithms. It also demonstrates promising practices for responsibly advancing algorithmic literacy across disciplines. Ultimately, fostering interdisciplinary awareness and multipronged educational initiatives can empower students to question algorithmic authority and biases.</p></div>","PeriodicalId":47762,"journal":{"name":"Journal of Academic Librarianship","volume":null,"pages":null},"PeriodicalIF":2.5000,"publicationDate":"2024-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Ethical dimensions of algorithmic literacy for college students: Case studies and cross-disciplinary connections\",\"authors\":\"Susan Gardner Archambault , Shalini Ramachandran , Elisa Acosta , Sheree Fu\",\"doi\":\"10.1016/j.acalib.2024.102865\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>This article addresses three key questions related to the ethical facets of algorithmic literacy. First, it synthesizes existing literature to identify six core ethical components, including bias, privacy, transparency, accountability, accuracy, and non-maleficence. Second, a crosswalk maps the intersections of these principles across the Association of College and Research Libraries' Framework for Information Literacy for Higher Education and the Association of Computing Machinery's Code of Ethics and Professional Conduct and Joint Statement on Principles for Responsible Algorithmic Systems. This analysis reveals significant overlap on issues like unfairness and transparency, helping prioritize topics for instruction. Finally, case studies showcase pedagogical strategies for teaching ethical considerations, informed by the crosswalk. Workshops for diverse undergraduates and computer science students employed reallife instances of algorithmic bias to prompt reflection on unintended harm, contestability, and responsible development. Pre-post surveys indicated expanded critical perspectives after the interventions. By systematically examining shared values and testing instructional approaches, this study provides practical tools to shape ethical thinking on algorithms. It also demonstrates promising practices for responsibly advancing algorithmic literacy across disciplines. Ultimately, fostering interdisciplinary awareness and multipronged educational initiatives can empower students to question algorithmic authority and biases.</p></div>\",\"PeriodicalId\":47762,\"journal\":{\"name\":\"Journal of Academic Librarianship\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.5000,\"publicationDate\":\"2024-03-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Academic Librarianship\",\"FirstCategoryId\":\"91\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0099133324000260\",\"RegionNum\":3,\"RegionCategory\":\"管理学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"INFORMATION SCIENCE & LIBRARY SCIENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Academic Librarianship","FirstCategoryId":"91","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0099133324000260","RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"INFORMATION SCIENCE & LIBRARY SCIENCE","Score":null,"Total":0}
Ethical dimensions of algorithmic literacy for college students: Case studies and cross-disciplinary connections
This article addresses three key questions related to the ethical facets of algorithmic literacy. First, it synthesizes existing literature to identify six core ethical components, including bias, privacy, transparency, accountability, accuracy, and non-maleficence. Second, a crosswalk maps the intersections of these principles across the Association of College and Research Libraries' Framework for Information Literacy for Higher Education and the Association of Computing Machinery's Code of Ethics and Professional Conduct and Joint Statement on Principles for Responsible Algorithmic Systems. This analysis reveals significant overlap on issues like unfairness and transparency, helping prioritize topics for instruction. Finally, case studies showcase pedagogical strategies for teaching ethical considerations, informed by the crosswalk. Workshops for diverse undergraduates and computer science students employed reallife instances of algorithmic bias to prompt reflection on unintended harm, contestability, and responsible development. Pre-post surveys indicated expanded critical perspectives after the interventions. By systematically examining shared values and testing instructional approaches, this study provides practical tools to shape ethical thinking on algorithms. It also demonstrates promising practices for responsibly advancing algorithmic literacy across disciplines. Ultimately, fostering interdisciplinary awareness and multipronged educational initiatives can empower students to question algorithmic authority and biases.
期刊介绍:
The Journal of Academic Librarianship, an international and refereed journal, publishes articles that focus on problems and issues germane to college and university libraries. JAL provides a forum for authors to present research findings and, where applicable, their practical applications and significance; analyze policies, practices, issues, and trends; speculate about the future of academic librarianship; present analytical bibliographic essays and philosophical treatises. JAL also brings to the attention of its readers information about hundreds of new and recently published books in library and information science, management, scholarly communication, and higher education. JAL, in addition, covers management and discipline-based software and information policy developments.