Sapia.ai, the global leader in ethical AI for assessment, released its independent third-party Disparate Impact Report, conducted by BLDS, LLC, a nationally recognized statistics and economics firm.
BLDS experts are frequently retained to analyze the workforce impact of planned management actions to minimize exposure to liability.
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With regards to Sapia.ai’s Chat Interview assessment tool, BLDS concluded that “using the Standardized Mean Difference, or SMD, and the Adverse Impact Ratio, or AIR, BLDS found no evidence of practically significant disparate impact” for sex or race/ethnicity assessed in the United States or Canada.
Sapia’s unique innovation is a text-chat interview and personalized feedback for every candidate. Candidates respond on their own time, thereby removing the pressures faced with a traditional job interview. Customers’ use of the Sapia assessment has consistently delivered a near-complete removal of bias and accelerated diversity outcomes.
The audit process
Employing a wide range of Disparate Impact Assessment tools and metrics, BLDS used two common metrics for determining whether there is evidence of practically significant disparate impact: the Standardized Mean Difference, or SMD, and the Adverse Impact Ratio, or AIR.
“Using the SMD, BLDS performed a total of 23 tests for protected groups on the North American models. Under the SMD test, BLDS found no evidence of practically significant disparate impact for any protected group assessed in the United States or Canada.
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“Using the AIR, BLDS performed a total of 49 tests for protected groups on the North American models. First, the use case where applicants advanced as the result of a ‘Yes’ recommendation was tested using the AIR. Second, the use case where applicants who received a ‘Yes’ or ‘Maybe’ recommendation was tested using the AIR. None of these tests revealed evidence of practically significant disparate impact for any protected group in the United States or Canada.”
Sapia.ai Founder and CEO Barb Hyman says the audit results demonstrate that Sapia.ai can make equality in hiring a reality.
“This audit provides independent corroboration that our smart chat is fair for all groups,” said Hyman.
“We have always believed that transparency is key to trust in AI and that’s why we have published our peer reviewed research in reputable journals.
“We also released the FAIR Framework (short for Fair AI for Recruitment), and were the first in the market to publicly share our own system for monitoring and mitigating bias in AI.
“It’s also why we don’t use any AI component to analyze video, audio, or CV data, or any data scraped from the web; it’s why we use explainable rule-based models, not classical machine learning models for scoring candidates.”
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