ethics-ai-analysis

Algorithmic fairness auditing measures demographic parity, equalized odds, and calibration gaps across sensitive groups using y_true, y_pred, and protected labels. It evaluates binary classification workflows to detect selection-rate disparities and disparate impact in automated decisions.

Disparate impact assessment evaluates the 80% or 4/5ths rule to flag potential EEOC-style concerns in automated decision-making. It measures whether selection rates for protected groups fall below 80% of the highest-selected group's rate, indicating algorithmic bias.

When equalized odds and demographic parity metrics conflict, you apply utilitarian, deontological, and virtue ethics frameworks to the AI ethics scenario. This value alignment approach produces a defensible conclusion for what should be done.

Yes, fairlearn integrates with pandas and scikit-learn to measure bias in credit scoring models. This skill requires these dependencies to compute fairness metrics over y_true, y_pred, and sensitive group labels for binary classification workflows.

The best way to check value alignment for hiring screening or parole risk assessment is applying utilitarian, deontological, and virtue ethics lenses to fairness audit results. This generates moral framework reasoning for alignment-focused decision guidance.

Calibration gaps occur when prediction probabilities do not match actual outcome rates uniformly across sensitive demographic groups. This skill measures these differences alongside demographic parity and equalized odds to identify algorithmic fairness violations.