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Title: Operationalizing Responsible AI & Governance at Scale in an evolving AI landscape
Abstract: As AI systems become more powerful and pervasive, the need to embed responsibility, safety, and governance into every layer of their development and deployment becomes critical. At a company like LinkedIn—where AI drives product experiences at massive scale—upholding member trust and safety is our top priority. Operationalizing Responsible AI (RAI) in this context is not just a technical challenge, but a core business and ethical imperative.
In this talk, I’ll share how we define, scale, and uphold RAI across LinkedIn’s ecosystem. I’ll walk through the methodologies we use to assess risk—from model training to post-deployment behavior—and how we support teams in mitigating those risks while continuing to innovate. I’ll also explore the operational and technical architecture of our AI Governance Program, and how we navigate tensions between speed and safety, compliance and creativity, and structure and adaptability.
Join us for a behind-the-scenes look at the evolving practice of Responsible AI in the age of LLMs, agents, and accelerated AI regulation.
Seminar Recording
Sakshi Jain
Engineering Director, Responsible AI and Governance
Sakshi Jain is Director of Engineering at LinkedIn, spearheading the Responsible AI and AI Governance efforts in the company. Her organization architects systems and technologies that prioritize Generative AI safety, fairness, and transparency in recommendation and classification systems. With a strong foundation in Physics from IIT Bombay and advanced studies in Machine Learning and Anti-abuse from UC Berkeley, Sakshi brings a unique blend of technical expertise and ethical vision. Previously, she spent six years developing scalable ML systems to combat fraud and abuse. Under her leadership, LinkedIn’s Responsible AI initiatives have made significant strides in creating ethical AI solutions that prioritize integrity and user trust.
About the Series: The HDSI Data Science in Industry Seminar series provides the opportunity for Harvard students, faculty, and postdoctoral fellows to hear directly from industry data scientists about the role data science plays in their organization, the methods and techniques being used, and their own career trajectory.