Bennett Borden is a lawyer and data scientist who has made significant contributions to the field of AI governance and algorithmic bias testing. With extensive experience in the U.S. intelligence community, he has built a reputation as a trusted AI counsel to major generative AI companies and dozens of Fortune 500 companies. Bennett's deep understanding of AI governance has helped him to establish programs in a variety of industries such as insurance, financial services, labor and employment, manufacturing, retail, health, and life sciences. Bennett is a leading authority on developing insight out of data, whether to help clients monetize and productize data, develop AI systems and algorithmic models in a legal and ethical manner, or conduct discovery and internal investigations. He also advises multinational clients regarding data privacy, security, and related regulatory compliance.
With a strong academic background, including studies at Oxford, Georgetown, and NYU, Bennett has earned a reputation as a highly skilled lawyer with expertise in algorithmic bias testing. He has helped numerous clients monitor and test their AI and automated decision-making systems for unintended bias, and his work in this area has been widely recognized. Bennett's contributions to the field have earned him the distinction of being named a Chambers ranked lawyer annually since 2015.
Bennett's legal expertise extends beyond algorithmic bias testing to include defending companies in the use of AI and automated decision-making systems, including high-profile cases such as a social media MDL case in the Northern District of California. He is a well-known legal expert in the area of AI and is able to provide clients with sound advice and legal representation. Harnessing the power of data is essential for helping clients drive value in their business operations and for telling their side of the story in litigation or regulatory investigations. Bennett advises the firm and its clients on the development and use of analytics models that enable insight, data storytelling, and economic value generation. Bennett’s groundbreaking research into the use of machine-based learning and unstructured data for organizational insight is now being put to work in data-driven early-warning systems for clients to detect and prevent corporate fraud and other misconduct.
In addition to his legal work, Bennett has authored numerous articles and publications on topics related to AI governance, algorithmic bias testing, and legal issues surrounding AI. His work has been widely cited and has helped to shape the conversation around these important issues. Bennett is a Scientific Advisor to NIST, a member of the National Conference of Lawyers and Scientists (NCLS) of the American Academy for the Advancement of Science and is a frequent speaker, author, and guest lecturer at universities and law schools, including, Georgetown University Law Center, the University of Virginia Law School, Temple University Law School, Brigham Young University and the University of Maryland College of Information Studies.
