Request Demo

pymetrics Targets Multiple Forms of Bias in Hiring

Watch video
Remove "Same as Me" Bias

Large applicant pools are often dramatically filtered with strategies to find “people like me” such as alumni campus recruiting and employee referral programs. This can mean that over 80% of candidates are never even seriously considered. Those who are ignored are significantly more likely to be underrepresented minorities.

Remove Assessment Bias
Remove Human Bias

Experimental studies have repeatedly shown that changing only the name on a resume (e.g., to signal race or gender) affects evaluations by HR personnel. Researchers have also found that implicit bias training is largely ineffective at improving the fairness of the hiring process.


Fairness in Hiring & Talent Management Drives Better Business Outcomes.

Designing Artificial Intelligence with Ethical Principles at the Forefront.

Case Study

Global Pharmaceutical Firm Drives Diversity
Professional Hiring
Pain Points
This client was looking to reduce time spent on lengthly behavioral interviews while removing bias and providing an enhanced candidate experience across their selection strategy for medium to high volume roles.
The client deployed Custom Success Models at the top of their recruitment funnel to provide a data point to support screening.
Increase in racial diversity at offer 
Projected Reduction in recruiting time with cut off use case
Candidate completion rate
Candidate Satisfaction rate