Artificial intelligence ELSI score for science and technology: a comparison between Japan and the US

Abstract

Artificial intelligence (AI) has become indispensable in our lives. The development of a quantitative scale for AI ethics is necessary for a better understanding of public attitudes toward AI research ethics and to advance the discussion on using AI within society. For this study, we developed an AI ethics scale based on AI-specific scenarios. We investigated public attitudes toward AI ethics in Japan and the US using online questionnaires. We designed a test set using four dilemma scenarios and questionnaire items based on a theoretical framework for ethics, legal, and social issues (ELSI). We found that country and age are the most informative sociodemographic categories for predicting attitudes for AI ethics. Our proposed scale, which consists of 13 questions, can be reduced to only three, covering ethics, tradition, and policies. This new AI ethics scale will help to quantify how AI research is accepted in society and which area of ELSI people are most concerned with.

Artificial Intelligence and the Disruption of Higher Education: Strategies for Integrations across Disciplines

“…In addition to industry AI ethics codes, process enhancements for decision-making, and emerging technology ethics roles, ELSI-based (ethics, legal, and social issues) approaches may connect industry efforts with a similar scale of past impact on Human Genome Project ethical reflection provided by university-based scholars in that field (cf. Hartwig et al, 2022;Calo, 2017). While some have argued that academic approaches "do not speak to the highly particular, concrete uses of data and AI," higher education should embrace conceptual and vocational training as a space for embedded experiences of technical decision-making based on the complex and unique nature of potential harms posed by these technologies (Blackman, 2020).…”

Intention to use technology for micro-credential programs: evidence from technology acceptance and self-determination model

“…Existing research considers attitude as a strong predictor of intention to do an action and represents an individual's judgment over the behavior under consideration (Sahu et al, 2020). However, attitudes can be either positive/negative depending on how an individual perceives using technology for online learning programs such as micro-credentials (Hartwig et al, 2022). A positive or favorable attitude is considered complementary to actual behavior realization via the intention to undertake the behavior (Petty et al, 2020).…”

Publics’ views on ethical challenges of artificial intelligence: a scoping review

“…To ensure the long-term social acceptability of AI as a trustworthy technology [70,71] was perceived as essential to support its use and to justify its implementation. In one study [72], the authors developed an AI ethics scale to quantify how AI research is accepted in society and which area of ethical, legal, and social issues (ELSI) people are most concerned with. Public trust in and acceptance of AI is claimed by social institutions such as governments, private sectors, industry bodies, and the science community, behaving in a trustworthy manner, respecting public concerns, aligning with societal values, and involving members of the publics in decision-making and public policy [46,48,[73][74][75], as well as in the responsible design and integration of AI technologies [52,76,77].…”

“…This could have multiple negative consequences for different groups based on ethnicity, disease, physical disability, age, gender, culture, or socioeconomic status [43,55,58,78,82,87], from the dissemination of hate speech [79] to the exacerbation of discrimination, which negatively impacts peace and harmony within society [58]. As there were crosscountry differences and issue variations in the publics' views of discriminatory bias [51,72,73], fostering diversity, inclusiveness, and cultural plurality [61] was perceived as crucial to ensure the transferability/effectiveness of AI systems in all social groups [60,94]. Diversity, nondiscrimination, and fairness were also discussed as a means to help reduce health inequalities [41,67,90], to compensate for human preconceptions about certain individuals [66], and to promote equitable distribution of benefits and burdens [57,71,80,93], namely, supporting access by all to the same updated and high-quality AI systems [50].…”

“…Some studies recognized the need for greater transparency that reduces the mystery and opaqueness of AI systems [71,82,101] and opens its "black box" [64,71,98]. Clear insights about "what AI is/is not" and "how AI technology works" (definition, applications, implications, consequences, risks, limitations, weaknesses, threats, rewards, strengths, opportunities) were considered as needed to debunk the myth about AI as an independent entity [53] and for providing sufficient information and understandable explanations of "what's happening" to society and individuals [43,48,72,73,78,102]. Other studies considered that people, when using AI tools, should be made fully aware that these AI devices are capturing and using their data [46] and how data are collected [58] and used [41,46,93].…”