Robust Explainable AI (XAI)
AI explanation, transparency, interpretability, and accountability methods suitable for complex deployed models.
Core metadata
- ID: explainable_ai_xai
- Era: Future
- First known date: 2035 (unknown)
- Region: Forecast / not yet broadly established
- Review status: source_checked
- Maturity: emerging
Prerequisites
- AI Safety & Alignment Methods (ai_safety_alignment_methods)
- Large Language Models (large_language_models)
- Model Evaluation Benchmarks (model_evaluation_benchmarks)
Dependents
- AI Model Auditing (ai_model_auditing)
- AI Value Alignment Problem (ai_value_alignment_problem)
- Artificial General Intelligence (AGI) (artificial_general_intelligence)
- Thought Control Interfaces (Direct Neural Command) (thought_control_interfaces_direct_neural_command)
Fields
Field lanes
- Artificial Intelligence & Machine Learning: Safety & Governance
Node sources
- Artificial Intelligence Risk Management Framework (AI RMF 1.0) (NIST, 2023, official_agency) • Supports: node, maturity
- Improving Transparency in AI Language Models: A Holistic Evaluation (Stanford HAI, 2022, weak_web) • Supports: node, maturity
Prerequisite edge evidence
Edge/source evidence summary:
- Prerequisite edges: 3
- Average edge confidence: 35%
- Prerequisite sources: 3
- speculative: 3
| Prerequisite | Type | Confidence | Evidence level | Note | Sources |
|---|---|---|---|---|---|
| Large Language Models (large_language_models) | speculative | 35% | speculative | Large Language Models is a plausible dependency for a forecast technology and should be treated as speculative. |
|
| Model Evaluation Benchmarks (model_evaluation_benchmarks) | speculative | 35% | speculative | Model Evaluation Benchmarks is a plausible dependency for a forecast technology and should be treated as speculative. |
|
| AI Safety & Alignment Methods (ai_safety_alignment_methods) | speculative | 35% | speculative | AI Safety & Alignment Methods is a plausible dependency for a forecast technology and should be treated as speculative. |
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