Evidence map›Paper›PMID 39959573›Full record

ArticleAI and ethics2025

The need for an empirical research program regarding human-AI relational norms.

Madeline G Reinecke, Andreas Kappes, Sebastian Porsdam Mann, Julian Savulescu, Brian D Earp

Abstract read
In one paragraph

Article in AI and ethics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Privacy and Human-AI Relationships.Philosophy & technology · 2025
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Madeline G ReineckeUniversity of Oxford, Oxford, UK.ORCID 0000-0002-5944-0209
Andreas KappesCity, University of London, London, UK.ORCID 0000-0003-0867-6630
Sebastian Porsdam MannUniversity of Oxford, Oxford, UK.ORCID 0000-0002-1867-2097
Julian SavulescuUniversity of Oxford, Oxford, UK.ORCID 0000-0003-1691-6403
Brian D EarpUniversity of Oxford, Oxford, UK.ORCID 0000-0001-9691-2888

Funding

Wellcome Trust
6 · The paper itself

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.

Indexed as

Human–AI interactionMoral psychologyNormsRelationships

Identifiers

PMID39959573
PMCPMC11828840

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.