Introduction to the topical collection on AI and responsibility

Conradie, Niël Henk; Kempt, Hendrik; Königs, Peter

doi:10.1007/s10676-022-09664-9

Cited by 1 publication

(1 citation statement)

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“…Like AI literacy, the field of responsible AI has evolved as AI systems have become more prominent in society. Responsible AI is closely associated with an ethical approach to the design and development of AI systems, which has led to fewer educational experiences and has mostly focused on the corporate and professional settings (Arrieta et al, 2020; Conradie et al, 2022; Dignum, 2019; Gupta et al, 2021; Lu et al, 2022; Wang et al, 2020). In contrast, AI literacy is strongly influenced by the field of AIEd, which has a long history of educational practice in primary and secondary education (Wilton et al, 2022).…”

Section: Conceptualization Of Responsible Aimentioning

confidence: 99%

Responsible AI literacy: A stakeholder-first approach

Domínguez Figaredo,

Stoyanovich

2023

Big Data & Society

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The need for citizens to better understand the ethical and social challenges of algorithmic systems has led to a rapid proliferation of AI literacy initiatives. After reviewing the literature on AI literacy projects, we found that most educational practices in this area are based on teaching programming fundamentals, primarily to K-12 students. This leaves out citizens and those who are primarily interested in understanding the implications of automated decision- making systems, rather than in learning to code. To address these gaps, this article explores the methodological contributions of responsible AI education practices that focus first on stakeholders when designing learning experiences for different audiences and contexts. The article examines the weaknesses identified in current AI literacy projects, explains the stakeholder-first approach, and analyzes several responsible AI education case studies, to illustrate how such an approach can help overcome the aforementioned limitations. The results suggest that the stakeholder-first approach allows to address audiences beyond the usual ones in the field of AI literacy, and to incorporate new content and methodologies depending on the needs of the respective audiences, thus opening new avenues for teaching and research in the field.

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