Global and Local Agendas of Computing Ethics Education

Hughes, Janet; Plaut, Ethan; Wang, Feng; Briesen, Elizabeth von; Brown, Cheryl L.; Cross, Gerry; Kumar, Viraj; Myers, Paul

doi:10.1145/3341525.3387423

Cited by 12 publications

(7 citation statements)

References 11 publications

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“…First, authors discussed imaginative modes of engagement where students sought to understand stakeholder perspectives. Many authors discussed near or local users, including indigenous groups (Hadisantono et al, 2020), but many discussed global users and the need for students to consider the perspectives or impacts on stakeholders across the globe (Berg & Lee, 2016; Bielefeldt et al, 2018; Clancy & Manuel, 2019; Fu et al, 2018; Huff & Martin, 1995; Hughes et al, 2020; Wang & Buckeridge, 2016). Authors noted that global considerations may inhibit actual interactions with stakeholders whom engineering products impact, but encouraged connecting ethics and DEI by fostering empathy for stakeholders across the globe (Naphan‐Kingery et al, 2019) or applying extant frameworks that are globally oriented (e.g., Sustainable Development Goals, see Tharakan, 2020).…”

Section: Resultsmentioning

confidence: 99%

“…Some students may resist engaging in ethics/DEI content because of the challenge and novelty of such discussions (e.g., see Rottmann & Reeve, 2020). In this sense, Hughes et al (2020) noted that the problems engineers respond to are "complex, changing, unpredictable problems all over the world that will be understood differently in different places, according to local culture and value systems" (p. 239). They encouraged educators to embrace this complexity and argued that computing professionals (and, we infer, engineers) "will benefit greatly from substantial engagement with critiques from across the world and across campuses" (p. 244).…”

Section: Engagement Strategies To Cultivate Ethics/dei In Engineeringmentioning

confidence: 99%

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How do ethics and diversity, equity, and inclusion relate in engineering? A systematic review

Hess,

Lin,

Whitehead

et al. 2023

J of Engineering Edu

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BackgroundThis paper begins with the premise that ethics and diversity, equity, and inclusion (DEI) overlap in engineering. Yet, the topics of ethics and DEI often inhabit different scholarly spaces in engineering education, thus creating a divide between these topics in engineering education research, teaching, and practice.PurposeWe investigate the research question, “How are ethics and DEI explicitly connected in peer‐reviewed literature in engineering education and closely related fields?”DesignWe used systematic review procedures to synthesize intersections between ethics and DEI in engineering education scholarly literature. We extracted literature from engineering and engineering education databases and used thematic analysis to identify ethics/DEI connections.ResultsWe identified three primary themes (each with three sub‐themes): (1) lenses that serve to connect ethics and DEI (social, justice‐oriented, professional), (2) roots that inform how ethics and DEI connect in engineering (individual demographics, disciplinary cultures, institutional cultures); and (3) engagement strategies for promoting ethics and DEI connections in engineering (affinity toward ethics/DEI content, understanding diverse stakeholders, working in diverse teams).ConclusionsThere is a critical mass of engineering education scholars explicitly exploring connections between ethics and DEI in engineering. Based on this review, potential benefits of integrating ethics and DEI in engineering include cultivating a socially just world and shifting engineering culture to be more inclusive and equitable, thus accounting for the needs and values of students and faculty from diverse backgrounds.

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Section: Resultsmentioning

confidence: 99%

Section: Engagement Strategies To Cultivate Ethics/dei In Engineeringmentioning

confidence: 99%

How do ethics and diversity, equity, and inclusion relate in engineering? A systematic review

Hess,

Lin,

Whitehead

et al. 2023

J of Engineering Edu

View full text Add to dashboard Cite

show abstract

“…Some educators have adopted “deep tech” approach to tech ethics (Ferreira and Vardi, 2021) integrating standard elements of technology ethics into a more holistic outlook that also embraces sociology, politics, social justice and development of potential socio-technical solutions. Others have worked to incorporate diverse value systems in developing a syllabus for teaching ethics, together with secular ethics frameworks (Qadir and Suleman, 2018; Hughes et al , 2020).…”

Section: State Of Practice In Artificial Intelligence Ethicsmentioning

confidence: 99%

Toward accountable human-centered AI: rationale and promising directions

Qadir

Islam

Al‐Fuqaha

2022

JICES

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Purpose Along with the various beneficial uses of artificial intelligence (AI), there are various unsavory concomitants including the inscrutability of AI tools (and the opaqueness of their mechanisms), the fragility of AI models under adversarial settings, the vulnerability of AI models to bias throughout their pipeline, the high planetary cost of running large AI models and the emergence of exploitative surveillance capitalism-based economic logic built on AI technology. This study aims to document these harms of AI technology and study how these technologies and their developers and users can be made more accountable. Design/methodology/approach Due to the nature of the problem, a holistic, multi-pronged approach is required to understand and counter these potential harms. This paper identifies the rationale for urgently focusing on human-centered AI and provide an outlook of promising directions including technical proposals. Findings AI has the potential to benefit the entire society, but there remains an increased risk for vulnerable segments of society. This paper provides a general survey of the various approaches proposed in the literature to make AI technology more accountable. This paper reports that the development of ethical accountable AI design requires the confluence and collaboration of many fields (ethical, philosophical, legal, political and technical) and that lack of diversity is a problem plaguing the state of the art in AI. Originality/value This paper provides a timely synthesis of the various technosocial proposals in the literature spanning technical areas such as interpretable and explainable AI; algorithmic auditability; as well as policy-making challenges and efforts that can operationalize ethical AI and help in making AI accountable. This paper also identifies and shares promising future directions of research.

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“…While academics have been studying the topics of teaching AI and AI ethics for more than half a century (e.g., Chand, 1974;Gehman, 1984;Martin et al, 1996;Applin, 2006;Ahmad, 2014), the systematic assessment of the topics, developments, and trends in teaching AI ethics is a relatively recent endeavor. However, most of the previous research that focused on a systematic analysis of teaching AI ethics suffered from one or more of the following limitations: 1) having a limited disciplinary scope (e.g., integration of ethics only in courses in machine-learning, Saltz et al, 2019;engineering, Bielefeldt et al, 2019;Nasir et al, 2021;human-computer interaction, Khademi & Hui, 2020;software engineering, Towell, 2003;or distributed systems, Abad, Ortiz-Holguin, & Boza, 2021); 2) having a limited geographical coverage and, as explained in Hughes et al (2020), Mohamed et al (2020), being biased towards Western cultures (e.g., Moller & Crick, 2018;Fiesler et al, 2020;Garrett et al, 2020;Raji et al, 2021;Homkes & Strikwerda, 2009); or 3) including courses taught at only one single level (e.g., introductory level, Becker & Fitzpatrick, 2019).…”

Section: Introductionmentioning

confidence: 99%

Get out of the BAG! Silos in AI Ethics Education: Unsupervised Topic Modeling Analysis of Global AI Curricula

Javed¹,

Nasir²,

Borit³

et al. 2022

jair

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The domain of Artificial Intelligence (AI) ethics is not new, with discussions going back at least 40 years. Teaching the principles and requirements of ethical AI to students is considered an essential part of this domain, with an increasing number of technical AI courses taught at several higher-education institutions around the globe including content related to ethics. By using Latent Dirichlet Allocation (LDA), a generative probabilistic topic model, this study uncovers topics in teaching ethics in AI courses and their trends related to where the courses are taught, by whom, and at what level of cognitive complexity and specificity according to Bloom’s taxonomy. In this exploratory study based on unsupervised machine learning, we analyzed a total of 166 courses: 116 from North American universities, 11 from Asia, 36 from Europe, and 10 from other regions. Based on this analysis, we were able to synthesize a model of teaching approaches, which we call BAG (Build, Assess, and Govern), that combines specific cognitive levels, course content topics, and disciplines affiliated with the department(s) in charge of the course. We critically assess the implications of this teaching paradigm and provide suggestions about how to move away from these practices. We challenge teaching practitioners and program coordinators to reflect on their usual procedures so that they may expand their methodology beyond the confines of stereotypical thought and traditional biases regarding what disciplines should teach and how. This article appears in the AI & Society track.

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Global and Local Agendas of Computing Ethics Education

Cited by 12 publications

References 11 publications

How do ethics and diversity, equity, and inclusion relate in engineering? A systematic review

How do ethics and diversity, equity, and inclusion relate in engineering? A systematic review

Toward accountable human-centered AI: rationale and promising directions

Get out of the BAG! Silos in AI Ethics Education: Unsupervised Topic Modeling Analysis of Global AI Curricula

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