Matching Algorithm Testing
Fairness auditing for candidate-job matching, recommendation systems, and talent marketplace algorithms.
The Challenge
Matching algorithms determine which candidates see which jobs—and which employers see which candidates. These invisible gatekeepers shape career opportunities for millions, but their decision-making processes are often opaque and biased.
When algorithms are trained on historical hiring data, they learn to replicate past patterns— including discriminatory ones. They may use features like location, education, or work history that serve as proxies for protected characteristics.
We test matching algorithms to expose hidden biases and help you build fairer talent systems.
What Can Go Wrong
Common fairness issues we test for in matching and recommendation algorithms
Historical Bias Encoding
Algorithms trained on past hiring data may perpetuate historical discrimination patterns.
Proxy Discrimination
Features like zip code, university, or club memberships may serve as proxies for race or class.
Feedback Loop Effects
If biased recommendations lead to biased hires, the system learns to reinforce its own biases.
Opportunity Inequality
Some candidates may never see relevant jobs because the algorithm never shows them.
Skill Inference Bias
AI may incorrectly infer skills from job titles, penalizing non-traditional career paths.
Recency Bias
Algorithms may over-weight recent experience, disadvantaging career returners.
Our Testing Methodology
A rigorous approach to evaluating algorithmic fairness in matching systems
Algorithm Audit
Understand the features, weights, and logic driving matching decisions.
Counterfactual Testing
Test how changes in protected characteristics affect match recommendations.
Outcome Analysis
Statistical analysis of which candidate groups see which opportunities.
Fairness Metrics
Apply multiple fairness criteria to identify discrimination patterns.
What You Get
Bias Analysis
Quantified evidence of any discriminatory patterns in matching outcomes.
Proxy Mapping
Identification of features that may serve as proxies for protected characteristics.
Fairness Roadmap
Actionable recommendations for improving algorithmic fairness.
Is Your Matching Algorithm Fair?
Discover what biases might be shaping your talent recommendations.
Request Assessment