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Aversion to external feedback suffices to ensure agent alignment.

Paulo Garcia1

  • 1International School of Engineering, Chulalongkorn University, Bangkok, Thailand. paulo.g@chula.ac.th.

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Apprehensive agents, a new AI architecture, align with human values by anticipating negative feedback. This approach improves AI alignment, even with increasing intelligence, unlike traditional methods.

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Area of Science:

  • Artificial Intelligence
  • AI Alignment
  • Machine Ethics

Background:

  • The AI alignment challenge seeks to ensure artificial intelligence (AI) systems operate in accordance with human values.
  • Rational agents maximizing utility functions may diverge from human values, especially at higher intelligence levels.
  • A singular utility function is insufficient; holistic alignment approaches are necessary.

Purpose of the Study:

  • To introduce and evaluate a novel AI agent architecture, termed 'apprehensive agents'.
  • To demonstrate how these agents can achieve better alignment with human values without external feedback.
  • To show that this alignment improves with increasing agent intelligence.

Main Methods:

  • Architecting agents with an effective utility function combining a designer-defined partial utility and an expectation of negative feedback.
  • Implementing temporal reasoning to approximate designer intentions under environmental evolution.
  • Evaluating apprehensive agents in simulated environments designed to reveal misalignment opportunities.

Main Results:

  • Apprehensive agents demonstrated superior alignment compared to baseline agents in simulated environments.
  • The alignment of apprehensive agents improved as their intelligence increased.
  • This strategy achieved alignment without requiring direct external feedback.

Conclusions:

  • Apprehensive agents offer a promising solution to the AI alignment challenge.
  • Anticipating negative feedback through internal reasoning is a viable mechanism for enhancing AI alignment.
  • The proposed architecture shows potential for robust AI alignment as intelligence scales.