Zhang, H., & Lee, I. A., & Moore, K. S., & Shah, S. A. (2023, June), Board 279: Ethics in Artificial Intelligence Education: Preparing Students to Become Responsible Consumers and Developers of AI  Paper presented at 2023 ASEE Annual Conference & Exposition, Baltimore , Maryland. 10.18260/1-2--42750

\bibitem{asee_peer_42750}
  Zhang, H., \& Lee, I. A., \& Moore, K. S., \& Shah, S. A. (2023, June), \emph{Board 279: Ethics in Artificial Intelligence Education: Preparing Students to Become Responsible Consumers and Developers of AI}
  Paper presented at 2023 ASEE Annual Conference \& Exposition, Baltimore , Maryland.
  10.18260/1-2--42750

Helen Zhang, Irene A. Lee, Katherine Strong Moore, and Sheikh Ahmad Shah.  "Board 279: Ethics in Artificial Intelligence Education: Preparing Students to Become Responsible Consumers and Developers of AI".  2023 ASEE Annual Conference & Exposition, Baltimore , Maryland, 2023, June.  ASEE Conferences, 2023.  https://peer.asee.org/42750 Internet.  30 Apr, 2024

\bibitem{asee_peer_42750}
  Helen Zhang, Irene A. Lee, Katherine Strong Moore, and Sheikh Ahmad Shah.
  "Board 279: Ethics in Artificial Intelligence Education: Preparing Students to Become Responsible Consumers and Developers of AI".
  \emph{2023 ASEE Annual Conference \& Exposition, Baltimore , Maryland, 2023, June}.
  ASEE Conferences, 2023.
  https://peer.asee.org/42750 Internet.  30 Apr, 2024

@INPROCEEDINGS{asee_peer_42750
author = "Helen Zhang, Irene A. Lee, Katherine Strong Moore, and Sheikh Ahmad Shah"
title = "Board 279: Ethics in Artificial Intelligence Education: Preparing Students to Become Responsible Consumers and Developers of AI"
booktitle = "2023 ASEE Annual Conference \& Exposition"
year = "2023"
month = "June"
address = "Baltimore , Maryland"
publisher = "ASEE Conferences"
note = {https://peer.asee.org/42750}
number = {10.18260/1-2--42750}
}

TY  - CPAPER
AB  - This poster reports a project entitled “Everyday AI for Youth” funded by the NSF ITEST program. The rapid expansion of Artificial Intelligence (AI) necessitates a need of educating students to become knowledgeable of AI and aware of its interrelated technical, social, and human implications. The latter (ethics) is particularly important to K-12 students because they have been Interacting with AI through everyday technology without realizing it. They may be targeted by AI generated fake content on social media and may have been victims of algorithm bias in AI applications of facial recognition and predictive policing. To empower them in the era of AI, education must support youth to recognize potential harms of AI. However, this is not easy. Ethics is complex and requires critical thinking of perspectives of various stakeholders involved in the design of AI, which is difficult for adolescents as they tend to think in an egocentric way.

In our project, we selected and sequenced a suite of ethics activities to expose students to different aspects of AI-related ethics issues. Informed by effective pedagogies and curricula to teach design ethics, these AI ethics lessons (1) stimulate students’ ethical imagination through designing algorithms for making the “best” PB&J sandwiches and imagining the definitions of “best PB&J sandwich” by different stakeholders (e.g., parents, children, dentists). By creating these personas, students begin to understand that users' priorities can change the design of the algorithm; (2) help students recognize ethical issues through investigating bias of existing technologies (e.g., Google Image search) and discussing whom the bias may impact; (3) help students analyze key ethical concepts and principles that are applicable to the AI field (e.g., the Blueprint for an AI Bill of Rights) and encourage them to take ethics seriously through case studies of how biased facial recognition technology harmed job applicants and misled police’s judgments; (4) increase student sensitivity to ethical issues by hands-on experiments of training AI models using unbalanced datasets and game playing of how deepfakes and misinformation spread out; (5) improve ethical judgment and willpower by engaging students in a culminating design project where they redesign the YouTube recommendation system. Students critiqued the technology, identified its sources of bias (e.g, selective stakeholders in the design, datasets), and created a plan outlining how to improve the system. To ensure that students develop an integrated understanding, these ethics lessons are embedded in their learning of technical aspects of AI.

Implementing these lessons showed a high engagement of all students, particularly female students of color, and an increase in their AI ethics awareness and knowledge. This suggests that our approach is highly promising in terms of preparing youth to become responsible and mindful consumers and future developers of AI technologies. Our work contributes to the AI and the design education field by providing a working learning sequence of how to teach ethical designs of AI to middle schoolers.

AU  - Helen Zhang
AU  - Irene A. Lee
AU  - Katherine Strong Moore
AU  - Sheikh Ahmad Shah
CY  - Baltimore , Maryland
DA  - 2023/06/25
PB  - ASEE Conferences
TI  - Board 279: Ethics in Artificial Intelligence Education: Preparing Students to Become Responsible Consumers and Developers of AI
UR  - https://peer.asee.org/42750
DO  - 10.18260/1-2--42750
ER  -