AI Engineering Podcast Inside the Black Box: Neuron-Level Control and Safer LLMs
Nov 16, 2025
Vinay Kumar, Founder and CEO of Arya.ai and head of Lexsi Labs, dives into the nuances of AI interpretability and alignment. He contrasts interpretability with explainability, highlighting the evolution of these concepts into tools for model alignment. Vinay shares insights on leveraging neuron-level editing for safer LLMs and discusses practical techniques like pruning and unlearning. He emphasizes the need for concrete metrics in alignment and explores the future role of AI agents in enhancing model safety, aiming for advanced AI that is both effective and responsible.
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Interpretability And Alignment Are Both Required
- Interpretability and alignment are distinct but both required to scale AI in regulated, mission-critical domains.
- Post-hoc explainability alone won't make models acceptable or auditable at enterprise scale.
Prefer Truthful, Scalable Interpretability Methods
- Use relevance-based and mechanistic methods (e.g., LRP, SAE, DL-Backtrace) rather than solely relying on surrogate explainers like SHAP and LIME.
- Evaluate scalability, truthfulness, and compute costs when choosing interpretability techniques.
Wolf-vs-Dog Shows Spurious Learning
- A classic example: a wolf-vs-dog classifier learned 'snow' as a proxy for wolves and misgeneralized.
- Vinay used this to illustrate why interpretability must verify the model learned the true causal features.