Experimental Studies on Perceptions of AI Intelligence and Morality, 2022-2025

Everett, Jim (2026). Experimental Studies on Perceptions of AI Intelligence and Morality, 2022-2025. [Data Collection]. Colchester, Essex: UK Data Service. 10.5255/UKDA-SN-858352

Machines powered by artificial intelligence (AI) are revolutionising the social world. We rely on AI when we check the traffic on Google Maps, when we connect with a driver on Uber, or when we apply for a credit check. But as the technological sophistication of AI increases, so too are the number and type of tasks that we rely on AI agents for - for example, to allocate scarce medical resources and assist with decisions about turning off life support, to recommend criminal sentences, and even to identify and kill enemy soldiers. AI agents are approaching a level of complexity that progressively requires them to embody not just artificial intelligence but also artificial morality, making decisions that would be directly described as moral or immoral if made by humans.

The increased use of AI agents has the potential for tremendous economic and social benefits, but for society to reap these benefits, people need to be able to trust these AI agents. While we know that trust is critical, we know very little about the specific antecedents and consequences of such trust in AI, especially when it comes to the increasing use of AI in morally-relevant contexts. This is important because morality is far from simple: We live in a world replete with moral dilemmas, with different ethical theories favouring different mutually exclusive actions. Previous work in humans shows that we use moral judgments as a cue for trustworthiness, so that it is not enough to just ask whether we trust someone to make moral decisions: we have to consider the type of moral decision they are making, how they are making it, and in what context. If we want to understand trust in AI, we need to ask the same questions - but there is no guarantee that the answers will be the same.

We need to understand how trust in AI depends depend on what kind of moral decision they are making (e.g. consequentialist or deontological judgments: Research Question #1) how they are making it (e.g. based on a coarse and interpretable set of decision rules or "black box" machine learning: Research Question #2), and in what relational and operational context (e.g. whether the machine performs close, personal tasks or abstract, impersonal ones, Research Question #3).

In this project I will conduct 11 experiments to investigate how trust in AI is sensitive to what moral decisions are made; how they are made; and in what relational contexts. I will use a number of different experimental approaches tapping both implicit and explicit trust and recruit a range of populations (British laypeople; trained philosophers and AI industry experts; a study with a convenience sample of participants all around the world; and an international experiment with participants representative for age and gender recruited simultaneously in 7 countries). At the end of the grant period, I will host a full-day interdisciplinary conference/workshop consisting of both academic and non-academic attendees to bring together experts working in AI together to consider the psychological challenges of programming trustworthy AI and the philosophical issues of using public preferences as a basis for policy relating to ethical AI.

This work will have important theoretical and methodological implications for research on the antecedents and consequences of trust in AI, highlighting the necessity of moving beyond simply asking whether we could trust AI to instead ask what types of decisions will we trust AI to make, what kinds of AI system we want making moral decisions, and in what contexts. These findings will have significant societal impact in helping public experts working on AI understand the how, when, and why people trust AI agents, allowing us to reap the economic and social benefits of AI that are fundamentally predicated on them being trusted by the public.

Data description (abstract)

We include data from nine preregistered experiments we investigated the psychological foundations of “orthogonality thesis”, examining whether people infer moral competence and moral motivation from intelligence in both artificial and human agents, and how these inferences affect judgments of trustworthiness and risk. Using between-subjects vignette studies, within-subjects “augmentation” paradigms, and explicit trait-change judgments, we manipulated descriptions perceived intelligence and morality while measuring downstream effects on trust and perceived danger.

Data was collected online via Qualtrics in 2023, with participants living in the UK recruited online via Prolific.ac.

For each study, we provide a Codebook with variable names and descriptions and a data file. Data was processed and analysed using R, and the data preparation script contains details of all exclusions and pre-processing.

Researchers wishing to use and cite this data should contact Dr. Jim A.C. Everett.

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