This work proposes an interactive, Python notebook-based in-class 'lab' assignment for courses related to AI (literacy), ML, or social impacts of computing. The ''AI Alignment Lab'' aims to surface the tensions, technical frontiers, stakeholders, and governance considerations involved in closing the principle-to-practices gap for responsible AI. Students are challenged to jailbreak and then align an open source large language model (LLM) relative to specific desired behavior. In doing so, students become more familiar with (some of) the limitations of current LLM safety mechanisms, and are encouraged to explore the difficult challenge of aligning AI models through a combination of fine-tuning, hard-coded filters, and policies to govern the use of AI.

The growth and permeation of artificial intelligence (AI) technologies across society has drawn focus to the ways in which the responsible use of these technologies can be facilitated through AI governance. Increasingly, large companies and governments alike have begun to articulate and, in some cases, enforce governance preferences through AI policy. In this context, overlapping jurisdictions and even contradictory policy preferences across private companies, local, national, and multinational governments create a complex landscape for AI policy which, we argue, will require AI developers able adapt to an evolving regulatory environment. Preparing CS students for the new challenges of an AI-saturated technology industry should therefore constitute a key priority for the computing curriculum. In this work, we will outline a proposed framework for integrating discussions on the nascent AI policy landscape into computer science courses. Building on recent literature on AI governance and our synthesis of AI policy efforts in the United States and European Union, we propose guiding questions to frame class discussions around AI policy in technical and non-technical (e.g., ethics) CS courses. Throughout, we emphasize the connection between normative policy demands and still-open technical challenges relating to their implementation and enforcement through code and governance structures. We conclude by highlighting opportunities to utilize our framework in practice, reflecting on our experiences using this framework in piloting curricular interventions in the 2024-2025 and 2025-2026 academic years.

ABET criteria for accreditation of undergraduate computer science (CS) degrees require universities to cover within their curricula topics including ''local and global impacts of computing solutions on individuals, organizations, and society,'' and to prepare their students to ''make informed judgments in computing practice, taking into account legal, ethical, diversity, equity, inclusion, and accessibility principles''. A growing body of research similarly identifies the need for CS programs to integrate ethics into their degree requirements, both through standalone ethics-related courses and embedded modules or case studies on the ethical impacts in 'technical' courses. The calls for increased attention to CS ethics education have become more pressing with the emergence of sophisticated consumer-ready AI technologies, which pose new ethical challenges in the forms of bias, hallucination, and autonomous decision-making. Yet it remains unclear whether current university curricula are adequately preparing future graduates to confront these challenges. This paper presents a systematic review of the degree requirements of 250 computer science bachelor's degree programs worldwide. We categorize each program according to whether a CS-related ethics course is offered and/or required by the department, finding that almost half of all universities we review do not offer any computing ethics courses, and only 33% of universities require students to take an ethics course to obtain their degree. We analyze differences among public US, private US, and non-US universities and discuss implications for curricular changes and the state of undergraduate computing ethics education.

The explosive growth of artificial intelligence (AI) technologies in everyday settings in recent years has highlighted the need to develop comprehensive policies to promote the ethical use of AI. As the next generation of AI developers and policymakers receive training on technical AI foundations, it is also important to examine how discussions around AI ethics and policy are (or are not) woven into existing computer science (CS) curricula. Thus, the perceptions of current college students studying AI are valuable in two ways: (1) to assess how AI ethics is currently being taught at the university level; and (2) to understand the attitudes of this new generation of AI thinkers towards AI, both in general and with respect to AI ethics.This paper summarizes the results of a preliminary survey of undergraduate CS students (n = 41) enrolled in a machine learning course at a large public university in the United States. We use our survey instrument to assess student attitudes towards AI, AI ethics, and AI policy. We find that while CS students are generally very positive about the benefit of AI and use AI tools regularly, they are nevertheless worried about the ethical impact of current and future AI technologies. Moreover, although nearly half of the students we surveyed would be interested in AI policy as a potential career path, only a third of respondents believed that their university courses were adequately preparing them to engage in discussions around AI policy and regulation. In this paper, we further evaluate these survey results and discuss implications for AI education at large.