Model Interpretability
Definition: Model interpretability means understanding how and why an AI system made a specific decision or prediction. It focuses on making the inner workings of AI models easier for people to explain and trust.
Example
If an AI recommends denying a loan or predicts a case outcome, model interpretability helps show which factors such income, jurisdiction, or precedent influenced that result.
Why It Matters?
Model interpretability is vital in law because lawyers and regulators must be able to explain how an AI reached its conclusions. Without it, firms risk using tools that are opaque or biased. Clear interpretability helps ensure transparency, fairness, and accountability, which are essential in legal and compliance settings.
How to Implement?
To implement interpretability, start by using AI models and tools that can show how they weigh different inputs. Add visualization or explanation tools such as SHAP or LIME to reveal what features influenced the outcome. In law, pair these explanations with human review so attorneys can verify that the reasoning behind each AI output aligns with professional judgment and ethical standards.