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Machine Thinking, Fast and Slow.

Jean-François Bonnefon1, Iyad Rahwan2

  • 1Toulouse School of Economics (TSM-R), CNRS, Université Toulouse Capitole, Toulouse, France.

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Summary
This summary is machine-generated.

Machines do not truly think fast and slow like humans. However, developers may simulate these cognitive modes, influencing how users interact with AI systems and potentially leading to misunderstandings among stakeholders.

Keywords:
algorithm aversionartificial intelligencedual-processmachine behaviormachine ethicstrust

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Area of Science:

  • Cognitive Science
  • Human-Computer Interaction
  • Artificial Intelligence Ethics

Background:

  • Human cognition is often described by dual-process theories, distinguishing fast, intuitive thinking from slow, deliberate reasoning.
  • The increasing sophistication of artificial intelligence (AI) prompts comparisons to human cognitive processes.
  • Stakeholders in AI development and deployment grapple with the implications of these comparisons.

Purpose of the Study:

  • To analyze the analogy between human fast and slow thinking and the "Fast and Slow Thinking" of machines.
  • To explore how this analogy influences engineers, user experience designers, regulators, ethicists, and end users.
  • To examine potential inspiration, challenges, and misconceptions arising from this comparison.

Main Methods:

  • Opinion article format, synthesizing perspectives from various stakeholders.
  • Conceptual analysis of the 'fast and slow' thinking analogy in AI.
  • Discussion of the interplay between human cognition models and machine capabilities.

Main Results:

  • Machines do not possess dual-process cognition akin to humans.
  • The emulation or simulation of human thinking modes in machines impacts user interaction.
  • The analogy can inspire innovation but also lead to misinterpretations and ethical challenges for all stakeholders.

Conclusions:

  • The 'fast and slow thinking' analogy in AI is a powerful but potentially misleading framework.
  • Careful consideration is needed to navigate the complex interplay between human cognitive models and AI development.
  • Understanding these dynamics is crucial for responsible AI design, regulation, and adoption.