Making AI decisions transparent and principled.
Explainable AI (XAI) is the field dedicated to making AI decisions understandable to humans. As AI systems make increasingly important decisions (medical diagnoses, loan approvals, legal recommendations), the ability to explain "why" becomes critical for trust, debugging, accountability, and regulatory compliance.
The field spans from post-hoc explanation methods (LIME, SHAP) that explain individual predictions, to inherently interpretable architectures, to the frontier of mechanistic interpretability β reverse-engineering what happens inside neural networks at the circuit level. The EU AI Act and similar regulations now mandate explainability for high-risk AI systems.
Why Explainability Matters
Trust (would you accept a cancer diagnosis from a black box?), debugging (finding model errors), accountability (who is responsible when AI is wrong?), regulation (EU AI Act requires explanations), and fairness (detecting bias in decisions).
LIME
Local Interpretable Model-agnostic Explanations β explains individual predictions by approximating the model locally with a simpler, interpretable model. Works with any model. "For this patient, age and blood pressure were the key factors."
SHAP
SHapley Additive exPlanations β uses game theory (Shapley values) to assign each feature its fair contribution to a prediction. Mathematically rigorous. Provides both local (per-prediction) and global (overall model) explanations.
Attention Visualization
Visualizing which input tokens/regions a transformer model focuses on when generating output. Informative but can be misleading β attention patterns don't always reveal the true reasoning process.
Mechanistic Interpretability
The frontier: reverse-engineering neural networks to understand the actual algorithms they implement. Anthropic's research identified specific circuits for math, language, and factual recall inside Claude. This is the deepest form of explainability.
Constitutional AI and Principles
Making AI behavior principled and transparent by training with explicit value statements. The model can articulate why it refuses or adjusts certain responses. Principles provide a human-readable "source code" for AI behavior.
Inherently Interpretable Models
Decision trees, linear models, and rule-based systems are interpretable by design. For high-stakes applications, some argue we should prefer interpretable models even at a cost to accuracy. Trade-off between capability and transparency.
Chain-of-Thought as Explanation
LLMs can explain their reasoning step by step. But are these explanations faithful to the actual internal process, or post-hoc rationalizations? Research suggests they are partially faithful but not fully reliable.
Regulatory Landscape
EU AI Act requires explainability for high-risk AI systems (healthcare, law enforcement, credit). US is developing sector-specific guidelines. The "right to explanation" may become a fundamental right in AI-affected decisions.
Challenges and Limitations
Some models may be too complex to explain faithfully. Explanations can be gamed (providing plausible but incorrect reasons). Balancing accuracy with interpretability remains an open challenge. Perfect explainability may be impossible for the most capable systems.
XAIExplainable AI β field dedicated to making AI decisions understandable and transparent to humans.
SHAPShapley Additive Explanations β game theory-based method for explaining individual model predictions.
Mechanistic InterpretabilityReverse-engineering neural networks to understand the actual algorithms and circuits they implement.
Right to ExplanationEmerging legal concept that people affected by AI decisions have the right to understand how those decisions were made.