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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 <jats:italic>country<\/jats:italic> and <jats:italic>age<\/jats:italic> 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.<\/jats:p>","DOI":"10.1007\/s00146-021-01323-9","type":"journal-article","created":{"date-parts":[[2022,1,22]],"date-time":"2022-01-22T00:04:11Z","timestamp":1642809851000},"page":"1609-1626","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Artificial intelligence ELSI score for science and technology: a comparison between Japan and the US"],"prefix":"10.1007","volume":"38","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6742-8843","authenticated-orcid":false,"given":"Tilman","family":"Hartwig","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5418-6726","authenticated-orcid":false,"given":"Yuko","family":"Ikkatai","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3421-2530","authenticated-orcid":false,"given":"Naohiro","family":"Takanashi","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4760-762X","authenticated-orcid":false,"given":"Hiromi M.","family":"Yokoyama","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,1,22]]},"reference":[{"key":"1323_CR1","unstructured":"Adebayo J, Kagal L, Pentland A (2015) The hidden cost of efficiency: fairness and discrimination in predictive modeling. 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