Reference
Reinecke, M., Kappes, A., Porsdam Mann, S., Savulescu, J. and Earp, B., (2025), 'The need for an empirical research program regarding human–AI relational norms', AI and Ethics, Vol: 5: 71–80 [PMC11828840]
Abstract
As artificial intelligence (AI) systems begin to take on social roles traditionally filled by humans, it will be crucial to understand how this affects people's cooperative expectations. In the case of human-human dyads, different relationships are governed by different norms: For example, how two strangers-versus two friends or colleagues-should interact when faced with a similar coordination problem often differs. How will the rise of 'social' artificial intelligence (and ultimately, superintelligent AI) complicate people's expectations about the cooperative norms that should govern different types of relationships, whether human-human or human-AI? Do people expect AI to adhere to the same cooperative dynamics as humans when in a given social role? Conversely, will they begin to expect humans in certain types of relationships to act more like AI? Here, we consider how people's cooperative expectations may pull apart between human-human and human-AI relationships, detailing an empirical proposal for mapping these distinctions across relationship types. We see the data resulting from our proposal as relevant for understanding people's relationship-specific cooperative expectations in an age of social AI, which may also forecast potential resistance towards AI systems occupying certain social roles. Finally, these data can form the basis for ethical evaluations: What relationship-specific cooperative norms we should adopt for human-AI interactions, or reinforce through responsible AI design, depends partly on empirical facts about what norms people find intuitive for such interactions (along with the costs and benefits of maintaining these). Toward the end of the paper, we discuss how these relational norms may change over time and consider the implications of this for the proposed research program.
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Funding
This research/project is supported by the National Research Foundation, Singapore under its AI Singapore Programme (AISG Award No: -AISG3-GV-2023-012), by the National Institute for Health and Care Research (NIHR) Oxford Health Biomedical Research Centre (Award NIHR203316), by the Wellcome Trust (Wellcome Centre for Ethics and Humanities 203132/Z/16/Z), and by the Novo Nordisk Foundation grant for a scientifically independent International Collaborative Bioscience Innovation & Law Programme (Inter-CeBIL Programme, Grant no. NNF23SA0087056). The views expressed are those of the authors and do not necessarily reflect those of the funding bodies or by the Department of Health and Social Care.
