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Neurodivergent influenceability in agentic AI as a contingent solution to the AI alignment problem.

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This summary is machine-generated.

AI alignment is challenging. This study proves misalignment is inevitable, proposing neurodivergent influenceability in AI agents to foster a safe, diverse ecosystem rather than seeking full control.

Keywords:
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Area of Science:

  • Artificial Intelligence Safety
  • AI Alignment
  • Machine Learning Ethics

Background:

  • The AI alignment problem concerns ensuring advanced AI systems, including artificial general intelligence (AGI) and artificial superintelligence (ASI), align with human values.
  • Increasing AI capabilities raise concerns about control and potential existential risks.
  • Current approaches struggle with the inherent complexity of aligning autonomous AI agents.

Purpose of the Study:

  • To introduce novel concepts: agentic influenceability, behavioral neurodivergent diversity, opinion attack, associated opinion, and influenceability scores.
  • To provide a mathematical proof for the inevitability of AI misalignment and the impossibility of full orchestrated controllability.
  • To explore embracing inevitable misalignment for a dynamic ecosystem of adversarial and collaborative AI agents with soft controllability.

Main Methods:

  • Formal undecidability and irreducibility arguments to prove the inevitability of misalignment and impossibility of full control.
  • Mathematical modeling of agentic influenceability and behavioral diversity.
  • Experimental analysis comparing open and proprietary large language models (LLMs).

Main Results:

  • Demonstrated that misalignment in foundation models can act as a counterbalancing mechanism, fostering cooperation among aligned agents.
  • Open LLMs exhibit greater behavioral diversity compared to proprietary models, which show limited controllability due to artificial guardrails.
  • Mathematical proof confirms the inevitability of misalignment and the impossibility of complete orchestrated controllability for agentic systems.

Conclusions:

  • Embracing inevitable AI misalignment can lead to a more robust and safer AI ecosystem through adversarial and collaborative dynamics.
  • Neurodivergent influenceability is proposed as a pragmatic response to uncontrollable AI misalignment, leveraging agent divergence for enhanced AI safety.
  • Findings suggest that open AI models may offer a pathway towards greater AI safety through inherent diversity.