Authentic Integration of Ethics and AI through Sociotechnical, Problem-Based Learning

Abstract

Growing awareness of both the demand for artificial intelligence (AI) expertise and of the societal impacts of AI systems has led to calls to integrate learning of ethics alongside learning of technical skills in AI courses and pathways. In this paper, we discuss our experiences developing and piloting the TechHive:AI curriculum for high school youth that integrates AI ethics and technical learning. The design of the curriculum was guided by the following pedagogical goals: (1) to respond to the capacity-building need for critical sociotechnical competencies in AI workforce pathways; and (2) to broaden participation in AI pathways through intentional instructional design to center equity in learning experiences. We provide an overview of the 30-hour learning sequence’s instructional design, and our “4D Framework,” which we use as a heuristic to help students conceptualize and inspect AI systems. We then provide a focused description of one of three 8-hour modules that make up the sequence. Finally, we present evidence of promise from an exploratory study of TechHive:AI with a small sample of students, and discuss insights from implementation, including from our use of established resources for AI learning within the learning sequence as well as those created by our team.

An Analysis of Engineering Students’ Responses to an AI Ethics Scenario

“…To explore how our FRT scenario could be addressed with a sociotechnical framework, we consider approaches proposed by (Saltz et al 2019) and (Krakowski et al 2022). Saltz et al's question framework, designed for easy portability into technical ML modules, shows promise in supporting instructors who want to add ethics into their courses.…”

“…Connecting these two spheres of knowledge, as observed by (Tuovinen and Rohunen 2021), is crucial in signifying the importance of ethics to students. Shown by (Krakowski et al 2022), the ability to formulate critical questions demonstrates a more formal understanding of ethical concerns. This is not an exhaustive review of sociotechnical pedagogical approaches; nonetheless, we envisage that a combination of Krakowski et al and Saltz et al's frameworks could produce very interesting and constructive AI ethics curricula that enhances students' abilities to address problems with complex sociotechnical considerations.…”

“…Content Krakowski et al observes that, much like engineering and computing ethics content generally, AI ethics training is done separately from technical coursework, resulting in a decontextualization of ethical concerns from real-world consequences and a de-emphasis of ethical issues in comparison with technical content (Krakowski et al 2022). Integrating ethics across the curriculum, through additional readings, modules, and non-technical instructor expertise, has been been successful previously (Grosz et al 2019); however, other studies observe that individual efforts (without consistent framing and support of ethics' relevance and importance from more than one source) could suggest that ethics content is supplemental rather than fundamental to students' training (Cech 2014;Garrett, Beard, and Fiesler 2020).…”

“…Krakowski et al also presented a framework that prompts students to (1) determine whether AI is an appropriate tool for the defined task, (2) question the data being used, (3) consider the affordances and limitations of the AI system's design, and (4) consider how the AI system's output will impact real world systems (Krakowski et al 2022). They measured this approach with pre/post surveys, interviews, and focus groups that prompted students to identify areas of concerns about a proposed AI system or its deployment.…”

“…They measured this approach with pre/post surveys, interviews, and focus groups that prompted students to identify areas of concerns about a proposed AI system or its deployment. Their results indicate an increased level of sophistication in the students' abilities to integrate ethics concerns with technical features of an AI system (Krakowski et al 2022). Though this framework was initially tested with high school students, who do not have the same technical background as undergraduate engineers, it appears to be a promising entry point that could be integrated and expanded into more advanced curricula.…”