Evidence map›Paper›PMID 36248519›Full record

ReviewFrontiers in psychology2022

What is new with Artificial Intelligence? Human-agent interactions through the lens of social agency.

Marine Pagliari, Valérian Chambon, Bruno Berberian

Abstract readReview
In one paragraph

Review in Frontiers in psychology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. Article
  5. Article
  6. Review
  7. Improved diagnostic performance of insertable cardiac monitors by an artificial intelligence-based algorithm.Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology · 2023
    Article
  8. 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

3 authors.

Marine PagliariInstitut Jean Nicod, Département d'Études Cognitives, École Normale Supérieure, Centre National de la Recherche Scientifique, Paris Sciences et Lettres University, Paris, France.
Valérian ChambonInstitut Jean Nicod, Département d'Études Cognitives, École Normale Supérieure, Centre National de la Recherche Scientifique, Paris Sciences et Lettres University, Paris, France.
Bruno BerberianInformation Processing and Systems, Office National d'Etudes et Recherches Aérospatiales, Salon de Provence, France.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In this article, we suggest that the study of social interactions and the development of a "sense of agency" in joint action can help determine the content of relevant explanations to be implemented in artificial systems to make them "explainable." The introduction of automated systems, and more broadly of Artificial Intelligence (AI), into many domains has profoundly changed the nature of human activity, as well as the subjective experience that agents have of their own actions and their consequences - an experience that is commonly referred to as sense of agency. We propose to examine the empirical evidence supporting this impact of automation on individuals' sense of agency, and hence on measures as diverse as operator performance, system explicability and acceptability. Because of some of its key characteristics, AI occupies a special status in the artificial systems landscape. We suggest that this status prompts us to reconsider human-AI interactions in the light of human-human relations. We approach the study of joint actions in human social interactions to deduce what key features are necessary for the development of a reliable sense of agency in a social context and suggest that such framework can help define what constitutes a good explanation. Finally, we propose possible directions to improve human-AI interactions and, in particular, to restore the sense of agency of human operators, improve their confidence in the decisions made by artificial agents, and increase the acceptability of such agents.

Indexed as

acceptabilityexplainable Artificial Intelligencehuman–AI interactionjoint actionsense of agencysocial agency

Identifiers

PMID36248519
PMCPMC9559368

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.