ethics-bias-check

A fairness audit evaluates ranking and scoring algorithms for disparate impact by checking implicit defaults, proxy bias from correlated features, and unequal error harms to ensure fair automated decisioning before launch.

Proxy bias in automated decisioning occurs when neutral input features correlate with protected demographic attributes, causing unfair scoring. Auditing training data and model inputs identifies these proxy variables to prevent discriminatory outcomes.

You need a fairness audit when your scoring algorithm ranks, filters, or automates decisions about people and demographic differences may emerge. It evaluates error-cost distribution and feedback-loop risks to prevent entrenched disparities.

Assess disparate impact in training data by evaluating representation gaps and feedback-loop risks that entrench disparities. Analyze whether system outputs skew future training inputs, amplifying unfair outcomes for affected demographic groups.

Yes, you can evaluate unequal error costs in recommender systems by determining who bears false positives versus false negatives. The audit produces explicit harm-focused outputs and checks whether affected people have meaningful recourse.

An automated ethics review cannot replace legal counsel or fully contextualize societal impact. It requires structured human input describing system inputs, outputs, and affected populations to effectively evaluate direct bias and feedback-loop risk.