arXiv:2508.11214v2 Announce Type: replace-cross Abstract: Explanations of cognitive behavior often appeal to computations over representations. What does it take for a system to implement a given computation over suitable representational vehicles within that system? We argue that the language of causality -- and specifically the theory of causal abstraction -- provides a fruitful lens on this topic. Drawing on current discussions in deep learning with artificial neural networks, we illustrate how classical themes in the philosophy of computation and cognition resurface in contemporary machine
This publication, aligning with current discussions in deep learning, addresses a foundational aspect of AI interpretability and capability. It reflects a growing need for understanding how AI systems achieve their computational feats.
Understanding the causal underpinnings of AI computation is crucial for developing more robust, explainable, and generalizable AI systems. It can unlock new paradigms for AI design and application beyond current limitations.
This theoretical advancement could change how AI is explained, validated, and potentially built, moving towards more interpretable and causally aware architectures.
- · AI researchers
- · Developers of explainable AI (XAI)
- · Companies seeking robust AI solutions
- · Black-box AI development approaches
Improved theoretical frameworks for AI interpretability and cognition.
Development of new AI models designed with inherent causal abstraction for greater transparency.
Accelerated adoption of AI in high-stakes domains due to enhanced reliability and explainability.
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Read at arXiv cs.CL