IA explicável (XAI)
Definição
A IA explicável visa tornar o comportamento do modelo compreensível: quais entradas ou características motivaram a decisão, ou o que o modelo "pensa" em termos humanos. This supports trust, debugging, and compliance.
Ele suporta segurança de IA (auditoria) e viés em IA (compreensão de resultados injustos). Required or recommended in regulated domains (por ex. credit, hiring, healthcare). Trade-off: post-hoc explanations (SHAP, LIME) are flexible but can be approximate; inherently interpretable models are limited in expressiveness.
Como funciona
Atribuição de características (por ex. SHAP, LIME) atribui pontuações de importância às características de entrada para uma previsão dada. Visualtion visualization shows which tokens or regions the model attended to. Natural language explanations (por ex. from an LLM or a dedicated module) describe the decisão in words. Inherently interpretable models (por ex. linear models, decisão trees, rule lists) are interpretable by projeto. Choice depends on the model type and use case: post-hoc methods work with black boxes but may not reflect the true mechanism; interpretable models are more faithful but less flexible. Evaluate explanations for fidelity (do they match the model?) and usefulness (do they help users or auditors?). Integrate with evaluation and bias audits where needed.
Casos de uso
Explainability matters when users or regulators need to understand why a model made a given decisão (compliance, trust, debugging).
- Explaining credit, hiring, or medical decisãos for compliance and users
- Debugging and improving model behavior via attributions
- Building trust and transparency in high-stakes applications
Documentação externa
- Interpretable Machine Learning (Molnar) — Free online book
- SHAP documentation