AI-induced hyper-learning in humans.


Journal

Current opinion in psychology
ISSN: 2352-2518
Titre abrégé: Curr Opin Psychol
Pays: Netherlands
ID NLM: 101649136

Informations de publication

Date de publication:
11 Sep 2024
Historique:
received: 10 06 2024
revised: 13 08 2024
accepted: 09 09 2024
medline: 30 9 2024
pubmed: 30 9 2024
entrez: 30 9 2024
Statut: aheadofprint

Résumé

Humans evolved to learn from one another. Today, however, learning opportunities often emerge from interactions with AI systems. Here, we argue that learning from AI systems resembles learning from other humans, but may be faster and more efficient. Such 'hyper learning' can occur because AI: (i) provides a high signal-to-noise ratio that facilitates learning, (ii) has greater data processing ability, enabling it to generate persuasive arguments, and (iii) is perceived (in some domains) to have superior knowledge compared to humans. As a result, humans more quickly adopt biases from AI, are often more easily persuaded by AI, and exhibit novel problem-solving strategies after interacting with AI. Greater awareness of AI's influences is needed to mitigate the potential negative outcomes of human-AI interactions.

Identifiants

pubmed: 39348730
pii: S2352-250X(24)00113-1
doi: 10.1016/j.copsyc.2024.101900
pii:
doi:

Types de publication

Journal Article Review

Langues

eng

Sous-ensembles de citation

IM

Pagination

101900

Informations de copyright

Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.

Déclaration de conflit d'intérêts

Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Auteurs

Moshe Glickman (M)

Affective Brain Lab, Department of Experimental Psychology, University College London, London, UK; Max Planck UCL Centre for Computational Psychiatry and Ageing Research, University College London, London, UK. Electronic address: mosheglickman345@gmail.com.

Tali Sharot (T)

Affective Brain Lab, Department of Experimental Psychology, University College London, London, UK; Max Planck UCL Centre for Computational Psychiatry and Ageing Research, University College London, London, UK; Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA. Electronic address: t.sharot@ucl.ac.uk.

Classifications MeSH