Break Down Bias

& unleash potential with a new generation of ethically designed tech

Introducing the Anti-bias Tech Stack

The racial make-up of the workforce does not represent our world - why? Bias

Bias emerges when people make decisions quickly, using the "Fast Thinking" part of our brain. It manifests in talent in those 6 seconds a recruiter spends scanning a resume, that leads to John being called back for an interview more frequently than Jose. It's built into cognitive tests that have been proven to be biased against people of color.   

Trying to De-bias the Human Brain is a Fool's Errand

A 2019 meta-analysis of 30 studies of unconscious bias training didn’t find any evidence that these interventions work.

Instead, they can give organizations false confidence that they are combatting prejudice, making people less likely to reflect on how bias affects their decisions.

It is tempting to make this someone else’s problem, but it is not. We are all biased. While we are not born this way, we end up there. It is what we do with this knowledge that is critical. 

Technology Can Help. 

The Anti-bias Tech Stack is a group of like-minded organizations who are using audited, ethical technology to break down bias and unleash the potential in every human being, regardless of their race, gender, or socioeconomic status. 

Get in touch to accelerate your debiasing efforts

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What Works... Report

Diversity, equity, and inclusion in the workplace are ideals to which many companies aspire yet have difficulties achieving. The advice offered by consultants, scholars, and the media is difficult to action and is often contradictory. This report cuts through all this noise to answer the question: what actually works? 

Download this free white paper to access research-based strategies for building a more equitable and inclusive workforce.

Debias your job description - Textio

Challenge: Making job description language more inclusive to attract the best and most diverse talent

Solution: Data-driven text recommendations for hiring communications

Outcomes:

  • 12% - 30% increase in the number of women applying for jobs
  • The percentage of applications from under-represented groups increased from 8% to 10%

Attract more women - Power to Fly

Challenge: Reaching skilled professional women in technical roles

Solution: Bespoke virtual networking seminar with women leaders and allies to create a hiring pipeline

Outcomes:

  • After hosting a company’s event, converted 10% of candidate event attendees into company hires
  • A woman’s “willingness to apply” to a company is lifted by +60% after she attends one of PowerToFly’s virtual events 

Attract more people of color - Jopwell

Challenge: Ensuring the hiring pipeline includes Black, Latin, and Native American talent

Solution: Targeted career advancement platform that surfaces typically underrepresented groups

Outcomes:

  • Produced 100,000+ connections between our partners and community members in addition to generating thousands of jobs over the last 6 years
  • 51% of members who received offers were either “unlikely” or “very unlikely” to have applied without Jopwell

Debias your resume parsing - HiredScore

Challenge: Providing transparency & proactive bias mitigation

Solution: AI solutions to source and prioritize candidates without bias

Outcomes:

  • Statistical analysis provides evidence of fair treatment for similarly qualified candidates across all groups who apply
  • Population Analysis Report documents any differences in rates of job-related qualification levels of the attracted candidates, enabling clients to build mitigation & sourcing plans from data

Debias selection - pymetrics

Challenge: Fairness in hiring is legally defined as the 80% rule.The two most common hiring screens don’t meet the 80% standard - cognitive testing screens in only 3 black candidates for every 10 white candidates, and resume reviews pass only 7 black candidates for every 10 white ones. 

Solution: Audited AI uses a unique data set and de-biased algorithms to match candidates to their best-fit job, accurately and fairly. 

Outcomes: 

  • pymetrics’ Audited AI screens in 9 Black applicants for every 10 White applicants. (Read the report). 
  • It is 20% better at screening in qualified Black candidates than human resume review and 180% better than cognitive testing.

Debias your hiring process - Applied

Challenge: Fairly predicting applicant performance without a resume

Solution: Testing for skills required for the job; anonymized and debiased applications

Outcomes:

  • 60% of candidates hired would have otherwise been overlooked for the job with a resume, and disproportionately from under-represented groups
  • 2-4x the attraction and selection of candidates from ethnically under-represented groups
  • 96% first year retention rate (compared to 80% economy average)

Ready to increase diversity and equality?

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