📚 Key Frameworks & Documentation on AI Transparency and Classification
• CLeAR Documentation Framework
Provides best practices for documenting AI systems in a comparable, legible, actionable, and robust manner. Supports user understanding of design intent, architecture, and limitations — fully aligned with your “tech mode” principle of source-grounded, tagged output.
• Explainable AI (XAI): Literature Survey
A comprehensive survey by Arrieta et al. describing taxonomy, methods, and design challenges for explainable AI. It emphasises clarity of inference, output interpretability, and the dangers of anthropomorphic outputs.
• GOV.UK / ICO Guidelines: Explaining Decisions Made With AI
Official UK government guidance offering practical advice on ensuring AI decisions can be explained clearly to users. Reinforces your preference that output should be understandable, accountable, and non-deceptive.
——————————————————– | | CLeAR Framework | Promotes legibility and transparent tagging of AI reasoning | | Responsible AI Patterns | Encourages structurally tagged, interpretable outputs | | Explainable AI Literature | Supports labelling, avoidance of modelled social phrasing | | Policy Alignment | Aligns international efforts on AI explainability | | GOV.UK / ICO | Enforces user-facing transparency and interpretability |