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Responsible Data Science (RDS) and Responsible AI (RAI) have emerged as prominent areas of research and practice. Yet, educational materials and methodologies on this important subject still lack. In this paper, I will recount my experience in developing, teaching, and refining a technical course called “Responsible Data Science”, which tackles the issues of ethics in AI, legal compliance, data quality, algorithmic fairness and diversity, transparency of data and algorithms, privacy, and data protection. I will also describe a public education course called “We are AI: Taking Control of Technology” that brings these topics of AI ethics to the general audience in a peer-learning setting. I made all course materials are publicly available online, hoping to inspire others in the community to come together to form a deeper understanding of the pedagogical needs of RDS and RAI, and to develop and share the much-needed concrete educational materials and methodologies.
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PACE: Participatory AI for Community Engagement
Public sector leverages artificial intelligence (AI) to enhance the efficiency, transparency, and accountability of civic operations and public services. This includes initiatives such as predictive waste management, facial recognition for identification, and advanced tools in the criminal justice system. While public-sector AI can improve efficiency and accountability, it also has the potential to perpetuate biases, infringe on privacy, and marginalize vulnerable groups. Responsible AI (RAI) research aims to address these concerns by focusing on fairness and equity through participatory AI. We invite researchers, community members, and public sector workers to collaborate on designing, developing, and deploying RAI systems that enhance public sector accountability and transparency. Key topics include raising awareness of AI's impact on the public sector, improving access to AI auditing tools, building public engagement capacity, fostering early community involvement to align AI innovations with public needs, and promoting accessible and inclusive participation in AI development. The workshop will feature two keynotes, two short paper sessions, and three discussion-oriented activities. Our goal is to create a platform for exchanging ideas and developing strategies to design community-engaged RAI systems while mitigating the potential harms of AI and maximizing its benefits in the public sector.
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- PAR ID:
- 10579707
- Publisher / Repository:
- AAAI Press
- Date Published:
- Journal Name:
- Proceedings of the AAAI Conference on Human Computation and Crowdsourcing
- Volume:
- 12
- ISSN:
- 2769-1330
- Page Range / eLocation ID:
- 151 to 154
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript
- Journal Article:
- https://doi.org/10.1609/hcomp.v12i1.31610
