Interpretability Goals › Interpretability in Practice: Common Workflows

To make the goals of interpretability more concrete, consider three common scenarios that arise in practice and how interpretability tools support each one.

Scenario 1: A language model is deployed in a clinical setting and occasionally produces incorrect medication dosage recommendations. The immediate concern is patient safety, and the question is: under what conditions does the model fail, and can the failure mode be characterized precisely enough to add a safeguard? An interpretability workflow might begin with instance-level attribution analysis on known failure cases to identify common patterns (does the model fail when the input uses abbreviations? when the dosage is expressed in unusual units?). It would continue with probing classifiers to check whether the model's layer representations encode quantity magnitudes correctly. If probing reveals that quantity representations are unreliable in specific layers, that is both a diagnostic and a potential target for intervention, either via fine-tuning or by routing quantity-related queries to a specialized module.

Scenario 2: A language model is being evaluated before deployment in a politically sensitive application. The concern is that the model may encode political biases that would systematically disadvantage some groups or perspectives. An interpretability workflow might use probing classifiers to test whether demographic attributes (political affiliation, nationality, religion) are encoded in the model's representations of queries and documents. It would use activation patching to test whether those encodings causally influence downstream outputs. It would use feature analysis on sparse autoencoder features to look for features that combine demographic proxies with evaluative valence. No single method is conclusive, but convergent evidence from multiple methods provides a reasonable audit.

Scenario 3: A researcher wants to understand how a model performs multi-step mathematical reasoning. The scientific question is whether the model executes the steps of a calculation sequentially (each layer implementing one operation) or retrieves the answer directly from a distributed representation of the problem. A workflow might use the logit lens to track when the model commits to an answer across layers: if the final answer appears in early layers for simple problems but only in late layers for complex ones, that suggests sequential processing. Activation patching could test whether intermediate computational states (partial products, intermediate sums) are causally necessary for the final answer. Mechanistic analysis of a small model doing simple arithmetic might fully characterize the circuit, giving a ground-truth algorithm to compare against.

These scenarios share a common structure: start with a hypothesis, choose methods matched to that hypothesis, run controlled experiments to test the hypothesis, and cross-validate across methods. Interpretability is most productive when treated as hypothesis-driven empirical research, not as visualization generation.