arXiv:2603.09787v2 Announce Type: replace-cross Abstract: Explainable artificial intelligence (XAI) aims to provide human-interpretable insights into the behavior of deep neural networks (DNNs), typically by estimating a simplified causal structure of the model. In existing work, this causal structure often includes relationships where the presence of a concept is associated with a strong activation of a neuron. For example, attribution methods primarily identify input pixels that contribute most to a prediction, and feature visualization methods reveal inputs that cause high activation of a t
The paper addresses a growing need for more transparent and trustworthy AI systems, particularly as deep neural networks become more complex and integrated into critical applications.
This research contributes to making AI decision-making processes more understandable and debuggable, which is crucial for widespread adoption and regulatory compliance, particularly in high-stakes environments.
Current XAI methods primarily focus on identifying contributions of present features; this new approach extends explainability to understanding why certain expected concepts are *not* activated, offering a more complete picture of model reasoning.
- · AI ethicists
- · Developers of safety-critical AI
- · Regulatory bodies
- · XAI researchers
- · Opaque AI systems
- · Developers neglecting explainability
Improved understanding and debugging capabilities for complex neural networks.
Increased trust and accelerated adoption of AI in sensitive domains due to enhanced explainability.
New certification standards and regulatory frameworks for AI that incorporate methods for explaining 'absent concepts'.
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Read at arXiv cs.LG