HiringBranch, the pioneer of AI-based assessments that measure soft skills, has successfully completed a comprehensive audit of its algorithms by BABL AI Inc., a leading AI systems analyst firm. This audit aligns with New York City Local Law 144, legislation that regulates automated employment decision tools to prevent bias, exclusion of qualified candidates, and legal risks.
Committed to transparency and fairness, HiringBranch continuously monitors its technology’s impact on hiring outcomes. Previous analyses found no significant differences in assessment scores between male and female candidates or between native and non-native English speakers.
In the most recent voluntary audit, HiringBranch and BABL AI conducted a thorough bias analysis focusing on demographic groups including gender, ethnicity, first language, and geographic location. “Our goal is to discover what a candidate can do, regardless of who they are,” said Assaf Bar-Moshe, PhD, Chief Research and Development Officer at HiringBranch. “The BABL audit confirms that we can do just that – hiring based purely on skills without introducing bias.”
BABL AI’s evaluation confirmed that HiringBranch meets all requirements of Local Law 144, including governance, risk assessment, and disparate impact analysis.
Conducting independent third-party audits is a natural progression for HiringBranch, reinforcing the company’s commitment to fair and effective hiring tools. Regular evaluations allow AI developers to refine their solutions and stay ahead of evolving regulations.
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