Can this skill help with GDPR compliance?

Yes, it includes specific features to generate GDPR-compliant explanations for automated decisions, helping fulfill the 'Right to Explanation' requirement.

How does it integrate with my development workflow?

It integrates directly with the SpecWeave framework, allowing you to generate explainability artifacts through Claude using commands like /ml:explain-model.

What libraries does Model Explainer use?

It leverages industry-standard interpretability frameworks including SHAP and LIME to provide both global model-level and local prediction-level explanations.

Does it support fairness and bias testing?

Yes, the skill can generate fairness reports to detect disparate impact or feature bias in protected attributes like race, gender, and age.

Model Explainer

Name: Model Explainer
Author: anton-abyzov

byanton-abyzov

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データサイエンスとML

Generates human-readable interpretability reports and explainability metrics for machine learning models using SHAP, LIME, and feature importance.

概要

Model Explainer transforms complex 'black-box' machine learning models into transparent, interpretable systems by providing deep insights into model behavior. It leverages industry-standard techniques like SHAP values and LIME to explain specific predictions, alongside global analysis tools like Partial Dependence Plots and feature importance rankings. This skill is essential for developers and data scientists building production-grade ML software who need to ensure regulatory compliance (such as GDPR or Fair Lending), debug model biases, and build stakeholder trust through clear, visual, and textual explanations of AI decision-making.

主な機能

Global model-level insights via feature importance and partial dependence plots.
Automatic generation of explainability artifacts including PNG plots and Markdown reports.
13 GitHub stars
Local prediction explanations using SHAP values and LIME for individual data samples.
Automated regulatory compliance reporting for GDPR 'Right to Explanation' and Fair Lending.
Bias detection and fairness reporting across sensitive protected attributes like age and gender.

ユースケース

Explaining financial decisions like loan rejections or fraud alerts to meet regulatory requirements.
Building stakeholder trust by providing human-readable rationales for automated AI decisions.
Debugging model performance by identifying unexpected feature interactions or hidden biases.

概要

主な機能

Global model-level insights via feature importance and partial dependence plots.
Automatic generation of explainability artifacts including PNG plots and Markdown reports.
13 GitHub stars
Local prediction explanations using SHAP values and LIME for individual data samples.
Automated regulatory compliance reporting for GDPR 'Right to Explanation' and Fair Lending.
Bias detection and fairness reporting across sensitive protected attributes like age and gender.

ユースケース

Explaining financial decisions like loan rejections or fraud alerts to meet regulatory requirements.
Building stakeholder trust by providing human-readable rationales for automated AI decisions.
Debugging model performance by identifying unexpected feature interactions or hidden biases.