The Algorithmic Bias Lab

The Algorithmic Bias Lab is the research division of BABL AI. Since 2018, it has been pioneering methodologies, standards, and best practices in the fields of algorithmic auditing and responsible AI. Explore our comprehensive repository of peer-reviewed research that has significantly shaped the approach at BABL AI.

BABL AI: A Framework for Assurance Audits of Algorithmic Systems

BABL AI: A Framework for Assurance Audits of Algorithmic Systems

BABL AI continues to be a leader when it comes to AI system audits, and our team of audit experts recently highlighted their deep knowledge and expertise in an academic paper. In "A Framework for Assurance Audits of Algorithmic Systems,” the BABL AI research team led by Chief Product Officer Khoa Lam, Senior Advisor Benjamin Lange, and Senior Consultant Borhane Blili-Hamelin discusses an AI assurance audit framework called the “criterion audit,” which they model after financial auditing practices. The research was also co-authored and supported largely by CEO Shea Brown, Chief Ethics Officer Jovana Davidovic, and Senior Advisor Ali Hasan. This work is BABL AI’s basis for their audit methodology for NYC AI Bias Audit Law.
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