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What the surveys actually show
The strongest opposition appears when an AI output can change a person’s livelihood, health, safety, status or freedom. The Bentley-Gallup Business in Society Survey in 2025 found concern about AI use in hiring at 83%, self-driving cars at 81% and AI recommending medical advice at 78%. The report said these were also the top three concerns in 2024.
Pew Research Center’s June 9–15, 2025 survey of 5,023 U.S. adults found that 61% wanted more control over how AI is used in their lives. In the same Pew research program, 62% had not too much or no confidence in the federal government’s ability to regulate AI effectively.
Distrust is not limited to the technology. A 2025 Gallup and Special Competitive Studies Project survey found that 60% somewhat or fully distrusted AI, while 80% favored government rules for AI safety and data security even if those rules slowed development.
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Hiring produces one of the clearest behavioral signals. In a 2023 Pew survey, 66% said they would not want to apply for a job at an employer that uses AI to help make hiring decisions; 32% said they would.
Why “detest” is too broad
“Detest” captures the intensity of opposition to certain kinds of delegation, but it does not describe a blanket ban on AI. Pew’s 2025 findings indicate greater comfort with practical uses such as detecting financial fraud and helping develop medicines. Respondents were more opposed when AI would judge relationships, make religious or creative decisions, or help govern the country.
The pattern is conditional skepticism: people tend to accept AI as an assistant when a task is narrow, useful and reversible, and resist it as an opaque authority when the result is difficult to challenge or touches identity and values. The surveys show the pattern, but they do not prove that one cause explains it.
Which decisions people are least willing to delegate
Hiring and employment
Hiring systems can affect income, professional identity and access to opportunity. Applicants may not know what data was used, whether the model recognized a proxy for race or disability, or how to correct an error. The 83% concern level in the 2025 Bentley-Gallup survey and the 66% refusal rate in Pew’s 2023 survey show how strongly people react when an algorithm becomes a gatekeeper.
Healthcare advice
Medical recommendations carry bodily and sometimes irreversible consequences. Even a technically capable system may lack a patient’s full history, fail to communicate uncertainty or make an error that is hard for a nonexpert to detect. Concern about AI recommending medical advice reached 78% in the 2025 Bentley-Gallup survey.
Driving and physical safety
Self-driving systems must act in real time when a person cannot review each choice first. That combination of high stakes, limited explainability during an emergency and difficult recovery from a mistake helps explain the 81% concern level recorded by Bentley-Gallup in 2025.
Rank #3
Relationships, religion, creativity and government
These areas involve personal meaning and collective authority rather than a single measurable objective. Pew’s 2025 work found greater opposition to AI judging relationships, making religious or creative decisions, or helping govern the country. People may see such delegation as surrendering judgment that should remain human even when no immediate physical harm is involved.
Why practical assistance feels different
People generally draw a boundary between support and substitution. Fraud detection can flag a transaction for review; it does not necessarily determine a person’s moral worth. Medicine-development tools can search possibilities while researchers remain responsible for testing and decisions. In both cases, the task can be narrow and the result can be checked or reversed.
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A practical test for acceptable delegation
The following framework organizes the concerns reflected in the surveys. It is an analytical guide, not a score assigned by any one poll.
| Question | Lower-risk assistance | Higher-resistance decision-making |
|---|---|---|
| How serious is a mistake? | Limited financial inconvenience or an easily corrected alert | Lost employment, medical harm, injury or loss of liberty |
| Can the result be reversed? | A person can review, undo or rerun the process | The opportunity or harm cannot realistically be restored |
| Can someone explain and appeal it? | Clear reasons, records and a human review path | Opaque output with no meaningful appeal |
| What data is exposed? | Necessary, limited data with understandable retention | Broad personal, health, behavioral or biometric data |
| How serious is bias risk? | Errors are detectable and do not determine access | Historical or proxy bias can systematically exclude people |
| Who is accountable? | A qualified human owns the final decision | Responsibility is shifted to “the algorithm” or a vendor |
| What kind of task is it? | Practical assistance, sorting or detection | A value judgment about identity, worth, relationships or civic power |
Do Americans want a human in the loop?
The 61% who wanted more control over AI use point toward more than a preference for a visible employee pressing a button. Meaningful control requires the ability to know when AI was used, understand the factors that mattered, challenge an outcome and obtain a timely human review. A nominal reviewer who cannot override the system does not provide that control.
Accountability also has to extend beyond the front-line operator. If a vendor’s model is inaccurate or discriminatory, the organization using it still needs procedures for auditing, documenting decisions, correcting records and compensating people harmed by errors. Public demand for regulation—80% in the 2025 Gallup and Special Competitive Studies Project survey—reflects the belief that individual users cannot negotiate those protections on their own.
What the evidence does not establish
The surveys do not identify one proven cause of opposition. Accuracy concerns, unfairness, privacy, job insecurity, loss of autonomy and weak institutional accountability may all contribute. Nor do the results show that Americans reject every automated decision, that every human decision is better, or that a human presence automatically makes an AI system safe.
Survey wording, timing and the specific use case matter. The figures above come from different organizations and years, so they should not be combined into a single approval score. They consistently indicate a boundary around high-stakes delegation, not universal hostility toward AI.
What organizations should do with this signal
- Use AI first for recommendation, detection and administrative support rather than unreviewable final judgments.
- Tell affected people when an AI system materially contributed to a decision.
- Provide understandable reasons, a human appeal route and enough time for correction.
- Test for disparate error rates and monitor performance after deployment, not only before launch.
- Minimize collected data and state how long it is retained and who can access it.
- Assign a named, qualified person or office responsibility for the outcome.
- Keep records that allow an organization to reconstruct what the system used and why a decision was made.
These measures do not guarantee trust, but they address the control and accountability conditions that distinguish acceptable assistance from unwanted authority.
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