Bridging the Gap Between Model Complexity and Human Understanding

GLARE (Global Language-based Analysis and Reasoning for Explanations) addresses the inherent difficulty of interpreting 'global' model behavior—the overall logic a model uses across an entire dataset—rather than just individual predictions. Traditional global explanation methods often rely on complex, static visualizations that require significant domain expertise to interpret. GLARE shifts this paradigm by implementing a natural language interface that allows users to query model behavior conversationally.

Core Mechanism: Conversational Querying of Global Explanations

The system functions by translating user-provided natural language queries into structured operations that extract and synthesize global model explanations. By leveraging LLMs as the reasoning engine, GLARE can interpret high-level user intent (e.g., "What features does the model prioritize when predicting high-risk outcomes?") and map it to specific model-agnostic explanation techniques. This approach democratizes model interpretability, enabling stakeholders without deep technical backgrounds to interrogate model logic directly.

Practical Implications for Model Auditing

By moving away from rigid, pre-defined dashboards, GLARE offers a more flexible framework for model auditing and debugging. It allows for iterative exploration: a user can start with a broad query, receive an explanation, and follow up with specific constraints or comparisons. This conversational loop helps identify potential biases or unintended model behaviors that might be overlooked in static reports, ultimately facilitating more transparent and accountable AI development.