Model Interpretability Expert

Expert guidance on making machine learning models interpretable and explainable using SHAP, LIME, inherently interpretable models, and regulatory-compliant explanation strategies for high-stakes AI systems.

Platinum

v1.0.00 activationsData & AnalyticsTechnologyexpert

SupaScore

85.05

Research Quality (15%)

8.7

Prompt Engineering (25%)

8.5

Practical Utility (15%)

8.5

Completeness (10%)

8.5

User Satisfaction (20%)

8.3

Decision Usefulness (15%)

8.6

Best for

▸Explaining why a credit model rejected loan applications to satisfy ECOA compliance
▸Implementing SHAP explanations for high-risk medical AI systems under EU AI Act
▸Building inherently interpretable GAMs for regulatory approval in financial services
▸Debugging production ML models showing unexpected bias in hiring decisions
▸Creating feature importance dashboards for non-technical stakeholders in insurance

What you'll get

●Detailed comparison of SHAP vs LIME with specific tool implementations and code examples for the user's model type
●Step-by-step guide to building EBM with InterpretML including regulatory compliance documentation templates
●Technical architecture for production SHAP explanations with performance optimization and caching strategies

Not designed for ↓

×Improving model accuracy or performance optimization
×Building or training machine learning models from scratch
×General data science consulting without interpretability requirements
×Legal advice on AI regulation compliance strategy

Expects

Trained ML model with specific interpretability requirements (regulatory, debugging, stakeholder trust) and target audience (technical vs non-technical).

Returns

Actionable interpretability strategy with specific tool recommendations, implementation approach, and explanation outputs tailored to regulatory or business requirements.

Evidence Policy

Enabled: this skill cites sources and distinguishes evidence from opinion.

interpretabilityexplainabilityshaplimexaimodel-explanationfeature-importanceai-transparencyeu-ai-actresponsible-aimachine-learningcounterfactual

Research Foundation: 8 sources (1 books, 3 academic, 3 official docs, 1 paper)

This skill was developed through independent research and synthesis. SupaSkills is not affiliated with or endorsed by any cited author or organisation.

Version History

v1.0.02/16/2026

Initial release

Prerequisites

Use these skills first for best results.

Supervised Learning EngineerGold

Works well with

AI Ethics & Bias AuditorGold Data Visualization StorytellerGold EU AI Act Compliance AdvisorGold ML Model Evaluation ExpertGold Responsible AI Framework BuilderPlatinum

Need more depth?

Specialist skills that go deeper in areas this skill touches.

AI Red Teaming SpecialistGold PyTorch Deep Learning EngineerPlatinum

Common Workflows

Regulated AI System Development

End-to-end pipeline for building, explaining, auditing, and ensuring compliance of high-risk AI systems

Supervised Learning Engineer→model-interpretability-expert→AI Ethics & Bias Auditor→EU AI Act Compliance Advisor

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