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A Workshop on the Ethical Design of AIs was convened in September and October 2022. Workshop participants hailed from a wide range of disciplines and application domains, and expressed interest in establishing partnerships across academia, industry, and government agencies, to address the challenges that were identified during the event. One of the outcomes of the workshop was a recommendation for a 2023 Convergence Accelerator Track on the Ethical Design of AIs (EDAIs). Suggested recommendations of themes and goals for the EDAIs Track include the following: (1) Human Centered Design methodologies around Values and Measures and Incentives. (2) Proto Ethical AIs: Algorithms or Systems or Pipelines across multiple domains. (3) Best Practices for the design of ethical AIs. (4) Workforce development and education and training. This report documents the activities of the EDAIs Workshop 

Scholars and public figures have called for improved ethics and social responsibility education in computer science degree programs in order to better address consequential technological issues in society. Indeed, rising public concern about computing technologies arguably represents an existential threat to the credibility of the computing profession itself. Despite these increasing calls, relatively little is known about the ethical development and beliefs of computer science students, especially compared to other science and engineering students. Gaps in scholarly research make it difficult to effectively design and evaluate ethics education interventions in computer science. Therefore, there is a pressing need for additional empirical study regarding the development of ethical attitudes in computer science students. Influenced by the Professional Social Responsibility Development Model, this study explores personal and professional social responsibility attitudes among undergraduate computing students. Using survey results from a sample of 982 students (including 184 computing majors) who graduated from a large engineering institution between 2017 and 2021, we compare social responsibility attitudes cross-sectionally among computer science students, engineering students, other STEM students, and non-STEM students. Study findings indicate computer science students have statistically significantly lower social responsibility attitudes than their peers in other science and engineering disciplines. In light of growing ethical concerns about the computing profession, this study provides evidence about extant challenges in computing education and buttresses calls for more effective development of social responsibility in computing students. We discuss implications for undergraduate computing programs, ethics education, and opportunities for future research. 

Abstract Designing effective and inclusive governance and public communication strategies for artificial intelligence (AI) requires understanding how stakeholders reason about its use and governance. We examine underlying factors and mechanisms that drive attitudes toward the use and governance of AI across six policy-relevant applications using structural equation modeling and surveys of both US adults (N = 3,524) and technology workers enrolled in an online computer science master’s degree program (N = 425). We find that the cultural values of individualism, egalitarianism, general risk aversion, and techno-skepticism are important drivers of AI attitudes. Perceived benefit drives attitudes toward AI use but not its governance. Experts hold more nuanced views than the public and are more supportive of AI use but not its regulation. Drawing on these findings, we discuss challenges and opportunities for participatory AI governance, and we recommend that trustworthy AI governance be emphasized as strongly as trustworthy AI.