Model Interpretability AI
Content
This cell addresses the principles and practices required to ensure that artificial intelligence models are explainable, transparent, and epistemologically sound. Model interpretability is essential for auditability, trust, and ethical deployment, especially in systems that interact with human cognition or decision-making.
It explores the balance between model complexity and human intelligibility, and how interpretability enhances governance, fairness, and coauthorship in human–AI ecosystems.
Essence
- Define interpretability as both a technical and epistemological necessity
- Examine explainable AI approaches (e.g., SHAP, LIME, feature attribution)
- Identify risks of opacity in complex models (e.g., deep neural networks)
- Advocate for traceable and inspectable learning pathways
- Emphasize the relationship between interpretability and digital ethics
Links
- MentisCell - Director Ethics Epistemology
- MentisCell - Metaepistemology AI Thinking Thought
- MentisCell - Director AI Digital Identity
- MentisCell - Knowledge Curator
- MentisCell - Data Culture Organizational Change
Tags
#mentiscell #interpretability #explainableAI #auditability #ethics #modularity #mentiscraft
Contributors
Created with support from Microsoft Copilot on 2025-07-18.
Based on design goals and epistemological inputs from Jorge Godoy.