Can this skill be used for regulatory compliance?

While it provides technical assessments of fairness and bias, it should be used as a support tool alongside legal and compliance reviews to meet specific regulatory standards.

Does it offer suggestions for fixing identified biases?

Yes, the skill generates reports that include specific mitigation strategies, such as data augmentation or re-sampling techniques, to help improve model fairness.

What kind of AI systems can this skill evaluate?

This skill can analyze a wide range of AI/ML models and datasets, including those used for classification, prediction, and recognition, to detect demographic bias and ethical risks.

When should I use the AI Ethics Validator?

You should use this skill whenever you are preparing a dataset for training, evaluating a model's impact on users, or performing a final ethical audit before deployment.

AI Ethics & Fairness Validator

Name: AI Ethics & Fairness Validator
Author: jeremylongshore

byjeremylongshore

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883

Data Science & ML

Analyzes AI/ML models and datasets for potential biases and ethical concerns to ensure responsible AI development.

About

The AI Ethics & Fairness Validator skill empowers Claude to perform automated audits of machine learning projects, focusing on ethical integrity and social fairness. By leveraging the ai-ethics-validator plugin, this skill identifies hidden biases in training data, evaluates model performance across different demographic groups, and provides detailed reports with mitigation strategies. It is an essential tool for developers and data scientists who want to build trustworthy, responsible AI systems while adhering to best practices in fairness and transparency.

Key Features

Detailed reporting on ethical implications
Automated bias detection for training datasets
Actionable mitigation recommendations for identified issues
Fairness evaluation across diverse demographic groups
Evaluation of fairness metrics for ML models
883 GitHub stars

Use Cases

Conducting ethical impact assessments before AI deployment
Evaluating loan application models for algorithmic bias
Auditing image recognition datasets for representation imbalances

About

Key Features

Detailed reporting on ethical implications
Automated bias detection for training datasets
Actionable mitigation recommendations for identified issues
Fairness evaluation across diverse demographic groups
Evaluation of fairness metrics for ML models
883 GitHub stars

Use Cases

Conducting ethical impact assessments before AI deployment
Evaluating loan application models for algorithmic bias
Auditing image recognition datasets for representation imbalances