Context/Background

A large organization is implementing an AI-powered resume screening and candidate ranking system to streamline its hiring process. Historical hiring data, which will be used to train the algorithm, may contain implicit biases that have led to underrepresentation of certain demographic groups in the organization. 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.

Source

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.

Metric/Acceptance Criteria

The hiring algorithm must:

  • Demonstrate statistical parity within ±5% across demographic groups (when comparing selection rates for different protected groups)
  • Remove or mask protected characteristics (including name, gender, age, address, and other potential proxies for protected attributes) during the initial screening phase
  • Achieve equal true positive rates (±3%) across all demographic groups when predicting candidate success based on validation against historical performance data
  • Include a bias detection component that continuously monitors for emergent biases and generates alerts when disparate impact exceeds a threshold of 80% (per the 4/5ths rule)
  • Provide transparent explanations for candidate rankings that focus solely on job-relevant qualifications (see explainability)
  • Maintain an audit log of all algorithmic decisions with sufficient detail to allow for retrospective bias analysis
  • Ensure that at least 90% of candidates recommended by the algorithm who proceed to interview are evaluated as qualified by human recruiters
  • Include a diverse validation dataset that represents the broader labor market demographics for each job category
  • Implement a “human-in-the-loop” design that requires human review before any final decisions are made