arXiv:2604.11467v2 Announce Type: replace-cross Abstract: Explainable AI (XAI) methods reveal which features influence model predictions, yet provide limited means for practitioners to act on these explanations. Activation steering of components identified via XAI offers a path toward actionable explanations, although its practical utility remains understudied. We introduce an interactive workflow combining SAE-based attribution with activation steering for instance-level analysis of concept usage in vision models, implemented as a web-based tool. Based on this workflow, we conduct semi-struct
The rapid advancement and integration of AI models necessitate more practical and actionable explainability methods for developers and practitioners to maintain control and drive improvement.
This development allows for a more direct interaction with AI's internal workings, moving beyond mere attribution to actual manipulation, which is critical for refining models and ensuring their reliability and ethical deployment.
The ability to directly 'steer' AI components based on explanations changes the debugging and development paradigm from passive understanding to active intervention, making AI more governable.
- · AI developers
- · ML engineers
- · Organizations deploying AI
- · Explainable AI research
- · Black box AI models
- · Purely attribution-based XAI tools
AI developers gain enhanced control over model behavior through interactive steering methods.
This improved control leads to more robust, reliable, and interpretable AI systems, accelerating their responsible deployment across various sectors.
The widespread adoption of actionable XAI could decrease the risk profile of advanced AI, potentially influencing regulatory frameworks and public trust.
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Read at arXiv cs.LG