HiringBranch, creators of the first AI-based assessments to measure soft skills, has engaged BABL AI Inc., a leading AI systems analyst firm, to audit its algorithms under New York City Local Law 144. This local AI legislation regulates automated employment decision tools that have the potential to perpetuate biases, exclude qualified candidates, and increase legal risks.
HiringBranch is committed to demonstrating the trustworthiness of its AI to end users, continuously conducting analyses to monitor the impacts of its technology and decision-making. These analyses revealed no significant differences in the assessment scores of male versus female candidates or in native versus non-native speakers of English.
In this latest audit, HiringBranch engaged in a voluntary bias analysis to identify potential disparities in performance across different demographic groups, such as gender, ethnicity, first language, and geographic location. “Our goal is to discover what a candidate can do, regardless of who they are,” says Chief Research and Development Officer at HiringBranch, Assaf Bar-Moshe, PhD. “The BABL audit confirms that we can do just that – we can hire candidates based on their skills without introducing bias into the process,” he concluded.
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Following the audit process, BABL AI concluded that HiringBranch satisfies Law 144’s requirement for bias. HiringBranch passed all sections, including those evaluating governance, risk assessment, and disparate impact quantification.
Completing an independent, third-party evaluation was an obvious next step for the HiringBranch team, and passing the New York City Local Law audit is the latest testament to the quality of the HiringBranch product. By conducting these types of analyses regularly, AI companies can identify possible improvements to enhance the fairness of their assessment tool alongside emerging AI regulations.
Source: BusinessWire
