Which Python libraries are required for this skill?

This skill utilizes industry-standard fairness assessment libraries such as Fairlearn and IBM's AI Fairness 360 (AIF360) to perform deep analytical checks.

What metrics does this skill calculate?

The skill calculates several key fairness metrics including demographic parity ratios, equalized odds, predictive parity, and calibration scores across different demographic groups.

Does it support regulatory compliance?

Yes, the reports generated help align your AI systems with guidelines like the EU Ethics Guidelines for Trustworthy AI and the IEEE Ethically Aligned Design principles.

What should I do if my dataset is missing sensitive attributes?

The skill suggests using proxy detection methods or requesting access to protected attributes under appropriate data governance to ensure a thorough fairness evaluation.

Can it help fix identified biases?

Yes, beyond detection, the skill provides specific mitigation recommendations such as rebalancing training data, applying algorithmic constraints, or adjusting decision thresholds.

AI Ethics & Fairness Validator

Name: AI Ethics & Fairness Validator
Author: BbgnsurfTech

byBbgnsurfTech

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Data Science & ML

Audits machine learning models and datasets to identify biases and ensure ethical compliance through fairness metrics and mitigation strategies.

About

The AI Ethics Validator is a specialized tool designed to help developers and data scientists identify and mitigate biases within machine learning systems. It automates the process of evaluating model predictions and training data across demographic groups using industry-standard metrics like demographic parity and equalized odds. By integrating with frameworks like Fairlearn and AIF360, the skill generates comprehensive reports that classify bias severity, recommend technical mitigations, and ensure alignment with ethical AI guidelines such as the EU Ethics Guidelines for Trustworthy AI. It is an essential utility for anyone building responsible AI that requires rigorous fairness auditing and transparent documentation.

Key Features

Generation of structured ethical compliance and validation reports
Calculation of core fairness metrics including demographic parity and equalized odds
1 GitHub stars
Seamless integration with fairness libraries like Fairlearn and AIF360
Automated bias detection across sensitive demographic attributes
Actionable mitigation recommendations for data rebalancing and model constraints

Use Cases

Auditing a production-ready ML model for unintended bias before deployment
Analyzing training datasets to ensure representative balance across race, gender, and age
Generating ethical compliance documentation for regulatory audits or stakeholder reviews

About

Key Features

Generation of structured ethical compliance and validation reports
Calculation of core fairness metrics including demographic parity and equalized odds
1 GitHub stars
Seamless integration with fairness libraries like Fairlearn and AIF360
Automated bias detection across sensitive demographic attributes
Actionable mitigation recommendations for data rebalancing and model constraints

Use Cases

Auditing a production-ready ML model for unintended bias before deployment
Analyzing training datasets to ensure representative balance across race, gender, and age
Generating ethical compliance documentation for regulatory audits or stakeholder reviews