What is equalized odds and how does it differ from equal opportunity?

Equalized odds requires both true positive rate (TPR) and false positive rate (FPR) to be equal across groups. Equal opportunity is a relaxed version requiring only equal TPR. Equal opportunity ensures qualified individuals from all groups have equal chances of positive outcomes, while equalized odds additionally ensures equal error rates for negative outcomes.

What is the 80% rule (four-fifths rule)?

The 80% rule, established by the EEOC in employment guidelines, states that selection rate for any protected group should be at least 80% of the rate for the group with the highest rate. Mathematically: min(selection_rate_B / selection_rate_A, selection_rate_A / selection_rate_B) ≥ 0.80. Violation suggests potential disparate impact requiring justification.

Can a model be fair on all metrics simultaneously?

Generally no. Research shows that satisfying multiple fairness criteria simultaneously is often mathematically impossible when base rates differ between groups. This is known as the 'impossibility theorem' of fairness. You must choose which fairness criterion matters most for your specific application and document the trade-offs.

What if my groups have different base rates?

Different base rates (different proportions of actual positives) create inherent tension between fairness metrics. You cannot simultaneously achieve demographic parity and calibration when base rates differ. Consider whether historical base rate differences reflect true differences or historical bias, and choose metrics accordingly.

How do I choose between fairness metrics?

Consider your context: Use demographic parity when you want equal representation regardless of qualifications. Use equal opportunity when you want qualified members of all groups to have equal success rates. Use predictive parity when positive predictions should be equally reliable across groups. Use calibration when probability estimates should be accurate for all groups.

What causes AI models to be unfair?

Common causes include: biased training data reflecting historical discrimination, proxy variables correlated with protected attributes (e.g., zip codes correlated with race), unequal representation in training data, optimization for overall accuracy at the expense of minority performance, and feedback loops that amplify initial biases.

How can I improve model fairness?

Strategies include: pre-processing (reweighting or resampling training data), in-processing (adding fairness constraints during training), post-processing (adjusting decision thresholds per group), removing or transforming biased features, collecting more balanced data, and using fairness-aware algorithms like adversarial debiasing.

What tools exist for AI fairness analysis?

Popular tools include: IBM AI Fairness 360 (comprehensive toolkit), Google What-If Tool (interactive exploration), Microsoft Fairlearn (Python library), Aequitas (bias audit toolkit), LIME and SHAP (for understanding feature importance by group), and custom implementations using confusion matrices as shown in this calculator.

Technology

AI Fairness Calculator

Analyze AI model fairness using industry-standard metrics including demographic parity, equalized odds, equal opportunity, and disparate impact. Compare model predictions across groups to detect bias and ensure fair machine learning systems.

Example Scenarios

Group A (Privileged/Reference Group)

Confusion matrix values for the reference group

True Positives

False Positives

True Negatives

False Negatives

Group B (Protected/Comparison Group)

Confusion matrix values for the protected group

True Positives

False Positives

True Negatives

False Negatives

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Evaluate AI Model Fairness Across Demographic Groups

The AI Fairness Calculator helps data scientists and ML engineers assess whether their models treat different demographic groups equitably. Analyze six industry-standard fairness metrics, check disparate impact compliance, and receive actionable recommendations to reduce bias. Essential for responsible AI deployment in hiring, lending, healthcare, and criminal justice applications.

What is AI Fairness and Why Does It Matter?

AI fairness ensures machine learning models don't discriminate against protected groups based on characteristics like race, gender, age, or disability. Even well-intentioned models can exhibit unfair behavior due to biased training data, proxy variables, or historical disparities. Fairness metrics quantify how differently a model treats various groups, enabling detection and mitigation of algorithmic bias. The field encompasses multiple definitions because different contexts prioritize different notions of fairness.

Disparate Impact Formula

Disparate Impact = P(Ŷ=1|Group B) / P(Ŷ=1|Group A) ≥ 0.80

Why Measure AI Fairness?

Legal Compliance

The disparate impact rule (80% rule) has legal standing in employment and lending decisions. Organizations can face lawsuits if their AI systems produce discriminatory outcomes, even without discriminatory intent. Proactive fairness assessment helps avoid legal exposure.

Ethical AI Development

AI systems increasingly affect people's lives through hiring decisions, loan approvals, healthcare recommendations, and criminal justice predictions. Ensuring fair treatment across groups is an ethical imperative for responsible AI development.

Business Reputation

Biased AI systems generate negative publicity and erode customer trust. Companies face backlash when algorithms discriminate against protected groups. Fairness testing protects brand reputation and customer relationships.

Model Quality Improvement

Fairness analysis often reveals data quality issues, feature engineering problems, or model limitations. Addressing fairness issues frequently improves overall model performance and generalization.

How to Use the AI Fairness Calculator

AI Fairness Use Cases

Hiring and Recruitment

Resume screening and candidate ranking systems must not discriminate based on protected characteristics. The 80% rule originated from employment discrimination law. Hiring AI requires careful fairness analysis to avoid disparate impact on gender, race, age, and disability status.

Credit and Lending Decisions

Loan approval algorithms must comply with fair lending regulations. Credit scoring models that produce different approval rates across racial or gender groups face regulatory scrutiny. Fairness metrics help ensure equitable access to credit.

Healthcare Risk Assessment

Medical AI systems for diagnosis, treatment recommendations, and resource allocation must work equitably across demographic groups. Healthcare disparities can be amplified by biased algorithms, making fairness critical for health equity.

Criminal Justice and Risk Assessment

Recidivism prediction and bail algorithms have faced criticism for racial bias. Tools like COMPAS demonstrated how seemingly neutral features can produce discriminatory outcomes. Fairness analysis is essential for criminal justice applications.

Insurance Underwriting

Insurance pricing and approval models must balance actuarial accuracy with fair treatment across protected groups. Regulations increasingly require fairness documentation for AI-driven insurance decisions.

Content Moderation and Recommendations

AI systems that curate content, recommend products, or moderate user-generated content should work fairly across user demographics. Biased content systems can reinforce stereotypes and exclude minority voices.

Frequently Asked Questions

Demographic parity (also called statistical parity) requires that the positive prediction rate is equal across groups: P(Ŷ=1|A=a) = P(Ŷ=1|A=b). Use it when you want equal selection rates regardless of group membership, such as ensuring equal interview rates in hiring. However, it may conflict with accuracy if base rates differ between groups.

Evaluate AI Model Fairness Across Demographic Groups

What is AI Fairness and Why Does It Matter?

Disparate Impact Formula

Disparate Impact = P(Ŷ=1|Group B) / P(Ŷ=1|Group A) ≥ 0.80