Stanford CS547 HCI Seminar | Winter 2026 | Visual and Algorithmic Interpretation for Responsible AI

Safety guardrails collapse suddenly rather than gradually

Visualizing the "safety basin" reveals that perturbing LLM parameters causes safety measures to remain stable initially, then break abruptly and completely, shooting to maximum unsafe levels without warning.

Fine-tuning triggers catastrophic safety failures

Even minimal harmful data mixed into fine-tuning datasets can push models off the "safety cliff," causing complete guardrail failure where models suddenly generate offensive or dangerous content.

Safety and capability failure modes differ fundamentally

While benchmark performance like MMLU scores degrade gradually as parameters shift, safety exhibits a binary collapse mechanism that standard numerical metrics fail to predict or visualize.

Binary data filtering is insufficient for real-world safety

Current "static safety shaping" methods that keep or drop entire training examples allow harmful segments to sneak through while discarding valuable safe content contained within mixed examples.

Segment-level analysis enables surgical data curation

The "Shape It Up" approach chops training examples into segments, identifies safe versus unsafe portions within single responses, and dynamically reweights loss functions to neutralize harmful content.

State-of-the-art safety without capability trade-offs

Dynamic safety shaping achieves superior safety retention compared to vanilla fine-tuning while maintaining original model capabilities across different LLM architectures and guardrail configurations.

Interactive tools make black boxes translucent

Scalable visualizations like LM Attributor and Concept Attention surface relevant model behaviors to help practitioners understand complex systems without overwhelming technical detail.

Interpretability bridges algorithmic and safety analysis

Connecting visual interpretation to vulnerability quantification reveals precisely how and why guardrails fail, enabling targeted interventions rather than blind trust in aggregate metrics.

Education tools democratize AI understanding

Interactive explainers such as Transformer Explainer and Diffusion Explainers help students and developers learn model internals, fostering responsible AI development practices through accessible visualization.