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Explainability Through Systematicity: The Hard Systematicity Challenge for Artificial Intelligence.

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This summary is machine-generated.

Artificial intelligence (AI) requires more than just explainability; it needs systematicity for consistent and coherent thought. This paper redefines systematicity, addressing challenges and proposing a dynamic framework for AI development.

Keywords:
CompositionalityConnectionismExplainabilityFunctions of systematizationInterpretabilityLanguage of thoughtPhilosophy of artificial intelligenceProductivitySystematicityXAI

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Area of Science:

  • Cognitive Science
  • Artificial Intelligence
  • Philosophy of Mind

Background:

  • Explainability is a key AI expectation, but not the sole criterion for advanced AI.
  • The "systematicity challenge" historically questioned connectionist AI's ability to achieve systematic thought.
  • A richer conception of systematicity, encompassing consistency and coherence, has been overlooked.

Purpose of the Study:

  • To propose a broader ideal for AI beyond explainability, focusing on systematicity.
  • To offer a conceptual framework distinguishing four senses of "systematicity of thought."
  • To re-evaluate the tension between connectionism and systematicity.

Main Methods:

  • Conceptual analysis of "systematicity of thought."
  • Distinguishing multiple senses of systematicity.
  • Examining rationales for systematization and their transferability to AI models.

Main Results:

  • A conceptual framework is presented that differentiates four senses of systematicity.
  • The perceived conflict between connectionism and systematicity is addressed.
  • Five rationales for systematization are identified and applied to AI, revealing the "hard systematicity challenge."

Conclusions:

  • AI's systematicity ideal is more demanding than previously understood.
  • A dynamic understanding of systematization is proposed, regulating AI's need for systematicity.
  • This framework guides how and when AI models should be made more systematic.