Fostering public AI literacy has been a growing area of interest at CHI for several years, and a substantial community is forming around issues such as teaching children how to build and program AI systems, designing learning experiences to broaden public understanding of AI, developing explainable AI systems, understanding how novices make sense of AI, and exploring the relationship between public policy, ethics, and AI literacy. Previous workshops related to AI literacy have been held at other conferences (e.g., SIGCSE, AAAI) that have been mostly focused on bringing together researchers and educators interested in AI education in K-12 classroom environments, an important subfield of this area. Our workshop seeks to cast a wider net that encompasses both HCI research related to introducing AI in K-12 education and also HCI research that is concerned with issues of AI literacy more broadly, including adult education, interactions with AI in the workplace, understanding how users make sense of and learn about AI systems, research on developing explainable AI (XAI) for non-expert users, and public policy issues related to AI literacy.
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Responsible AI literacy: A stakeholder-first approach
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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- NSF-PAR ID:
- 10514463
- Publisher / Repository:
- Big Data & Society
- Date Published:
- Journal Name:
- Big Data & Society
- Volume:
- 10
- Issue:
- 2
- ISSN:
- 2053-9517
- Subject(s) / Keyword(s):
- responsible AI AI education ethical AI AI literacy AI fairness AI accountability
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1177/20539517231219958
