Principles of XAI
Explainable AI focuses on developing techniques that allow human users to comprehend and trust the results generated by machine learning models. The objective is to mitigate the "black box" problem prevalent in deep neural architectures.
Core Methodologies
- Post-hoc Interpretability: Utilizing algorithms such as SHAP or LIME to identify feature importance in pre-trained models.
- Intrinsically Interpretable Architectures: Utilizing designs that are inherently transparent, such as Decision Trees or KANs.
- Reasoning Traces: Implementing execution logs that provide formal or natural language rationales for specific agent actions.
Historical Milestones
- MYCIN (1970s): An early expert system that could explain its medical recommendations by tracing through its rule-based logic.
- Knowledge-Based Systems: The heritage of AI that prioritized explicit rule representations, providing a template for modern auditable reasoning.
- Visual Analytics: Research into using interactive visualization to help humans understand the state space of complex models.
- Counterfactual Explanations: Providing examples of how input must change to alter an output, based on early work in philosophy and causality.
Operational Requirement
The implementation of XAI enables the auditability of autonomous systems. Decision paths are recorded and linked to formal specifications, allowing for the retrospective verification of logic and evidence utilization.