AI-induced hyper-learning in humans.

Journal: Current opinion in psychology
PMID: 39348730

Abstract

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.

Authors

  • Moshe Glickman
    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
    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.

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