Context

A large organization is implementing an AI-powered resume screening and candidate ranking system to streamline its hiring process. Historical hiring data may contain implicit biases that have led to underrepresentation of certain demographic groups. The system must be designed to identify and mitigate these biases to ensure fair consideration of all qualified candidates regardless of gender, ethnicity, age, or other protected characteristics.

Trigger

The hiring algorithm processes resumes and application materials submitted through the company’s applicant tracking system, comparing them against job requirements and historical hiring patterns to rank candidates for human review.

Acceptance Criteria

• Statistical parity within ±5% across demographic groups (selection rates for protected groups)
• Protected characteristics (name, gender, age, address, proxies) removed or masked during initial screening phase
• Equal true positive rates (±3%) across all demographic groups when predicting candidate success
• Bias detection component continuously monitors for emergent biases and alerts when disparate impact exceeds 80% threshold (4/5ths rule)
• Transparent explanations for candidate rankings focus solely on job-relevant qualifications (see explainability)
• Audit log maintained for all algorithmic decisions with sufficient detail for retrospective bias analysis
• At least 90% of algorithm-recommended candidates who proceed to interview evaluated as qualified by human recruiters
• Diverse validation dataset represents broader labor market demographics for each job category
• “Human-in-the-loop” design requires human review before any final decisions made