Computing ethics instruction is a vital aspect of the undergraduate computing curriculum. It has received greater focus in recent years driven in part by concerns about the societal impacts of computing technologies such as social media and artificial intelligence. The increased attention provides an opportunity, even an imperative, to examine and rethink common practices. To support our understanding of current practices in computing ethics education, we surveyed 318 computing educators in the United States (U.S.), including 56 who have never taught ethics. The survey included questions about ethics teaching methods and challenges the instructors confronted. We show that ethical frameworks are frequently taught yet teaching them is regarded as one of the least important learning outcomes, and that respondents largely do not consider author demographics when selecting readings for their ethics classes, which could limit the diversity in perspectives present in the course. We conclude with recommendations for improving teaching methods, materials selection, and deployment strategies in computing ethics education, and discuss their implications for promoting more inclusive computing ethics education curricula in the U.S.

This article presents the Political Deepfakes Incidents Database (PDID), a collection of politically-salient deepfakes, encompassing synthetically-created videos, images, and less-sophisticated `cheapfakes.' The project is driven by the rise of generative AI in politics, ongoing policy efforts to address harms, and the need to connect AI incidents and political communication research. The database contains political deepfake content, metadata, and researcher-coded descriptors drawn from political science, public policy, communication, and misinformation studies. It aims to help reveal the prevalence, trends, and impact of political deepfakes, such as those featuring major political figures or events. The PDID can benefit policymakers, researchers, journalists, fact-checkers, and the public by providing insights into deepfake usage, aiding in regulation, enabling in-depth analyses, supporting fact-checking and trust-building efforts, and raising awareness of political deepfakes. It is suitable for research and application on media effects, political discourse, AI ethics, technology governance, media literacy, and countermeasures.

AI-related laws, standards, and norms are emerging rapidly. However, a lack of shared descriptive concepts and monitoring infrastructure undermine efforts to track, understand, and improve AI governance. We introduce AGORA (the AI Governance and Regulatory Archive), a rigorously compiled and enriched dataset of AI-focused laws and policies encompassing diverse jurisdictions, institutions, and contexts related to AI. AGORA is oriented around an original taxonomy describing risks, potential harms, governance strategies, incentives for compliance, and application domains addressed in AI regulatory documents. At launch, AGORA included data on over 330 instruments, with new entries being added continuously. We describe the manual and automated processes through which these data are systematically compiled, screened, annotated, and validated, enabling deep, efficient, and reliable analysis of the emerging AI governance landscape. The dataset, supporting information, and analyses are available through a public web interface (https://agora.eto.tech) and bulk dataset.

Computing Ethics (CE) courses are an increasingly important component of the undergraduate computing curriculum because of the outsized influence of computing on society. CE encompasses topics from multiple disciplines including the humanities; however, it is typically taught by educators within a Computer Science (CS) department in most undergraduate institutions in the United States, potentially leading to a less than comprehensive CE education for students. We surveyed 318 computing educators in US higher education to investigate CS educators' perceptions of how CE topics should ideally be taught. Most of our respondents thought that CE should be taught by a multidisciplinary team of instructors, and further that it should be taught both as a standalone course and embedded in other courses. Our research provides insights into ways to improve CE education that result in a better student experience.

Since 2016, more than 80 AI ethics documents - including codes, principles, frameworks, and policy strategies - have been produced by corporations, governments, and NGOs. In this paper, we examine three topics of importance related to our ongoing empirical study of ethics and policy issues in these emerging documents. First, we review possible challenges associated with the relative homogeneity of the documents' creators. Second, we provide a novel typology of motivations to characterize both obvious and less obvious goals of the documents. Third, we discuss the varied impacts these documents may have on the AI governance landscape, including what factors are relevant to assessing whether a given document is likely to be successful in achieving its goals.

Artificial intelligence (AI) enabled products and services are becoming a staple of everyday life. While governments and businesses are eager to enjoy the benefits of AI innovations, the mixed impact of these autonomous and intelligent systems on human well-being has become a pressing issue. The purpose of this article is to review one of the first international standards focused on the social and ethical implications of AI: The Institute of Electrical and Electronics Engineering's (IEEE) Standard (Std) 7010-2020 Recommended Practice for Assessing the Impact of Autonomous and Intelligent Systems on Human Well-being. Incorporating well-being factors throughout the lifecycle of AI is both challenging and urgent and IEEE 7010 aims to provide guidance for those who design, deploy, and procure these technologies. Before introducing IEEE 7010, we consider possible benefits of an approach for AI centered around well-being and the measurement of well-being data. Next, we critically examine how the standard relates to approaches and perspectives in place in the AI community. Finally, we indicate where future efforts are needed for IEEE 7010 to better achieve its ambitions.