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.more » « less
- Award ID(s):
- NSF-PAR ID:
- Publisher / Repository:
- Oxford University Press
- Date Published:
- Journal Name:
- Science and Public Policy
- Medium: X Size: p. 161-176
- ["p. 161-176"]
- Sponsoring Org:
- National Science Foundation
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Background and Aims
Our ability to combat the opioid epidemic depends, in part, on dismantling the stigma that surrounds drug use. However, this epidemic has been unique and, to date, we have not understood the nature of public prejudices associated with it. Here, we examine the nature and magnitude of public stigma toward prescription opioid use disorder (OUD) using the only nationally representative data available on this topic.
General Social Survey (GSS), a cross‐sectional, nationally representative survey of public attitudes.
United States, 2018.
A total of 1169 US residents recruited using a probability sample.
Respondents completed a vignette‐based survey experiment to assess public stigma toward people who develop OUD following prescription of opioid analgesics. This condition is compared with depression, schizophrenia, alcohol use disorder (AUD) and subclinical distress using multivariable logistic or linear regression.
Adjusting for covariates (e.g. race, age, gender), US residents were significantly more likely to label symptoms of OUD a physical illness [73%, confidence interval (CI) = 66–80%;
P< 0.001] relative to all other conditions, and less likely to label OUD a mental illness (40%, CI = 32–48%; P< 0.001). OUD was significantly less likely to be attributed to bad character (37%, CI = 30–44%; P< 0.001) or poor upbringing (17%, CI = 12–23%; P< 0.001) compared with AUD. Nonetheless, perceptions of competence associated with OUD (e.g. ability to manage money; 41%, CI = 33–49%; P< 0.01) were lower than AUD, depression and subclinical distress. Moreover, willingness to socially exclude people with OUD was very high (e.g. 76% of respondents do not want to work with a person with OUD), paralleling findings on traditional targets of strong stigma (i.e. AUD and schizophrenia). Conclusions
US residents do not typically hold people with prescription opioid use disorder responsible for their addiction, but they express high levels of willingness to subject them to social exclusion.