Women in Big Data and LinkedIn hosted an empowering event The Responsible AI at Scale in LinkedIn HQ in Sunnyvale, CA on March 13^th, 2025, for people passionate about ethics, transparency and shaping the AI technologies of the future. With top industry experts, networking opportunities, and the LinkedIn team

offering ‘Rock Your Profile’ guidance, personal branding tips, and professional headshots, attendees enjoyed a full agenda.

The LinkedIn Sr Engineering Manager Swathi Koundinya and Women in Big Data confounder Radhika Rangarajan kicked off the event sharing WiBD’s journey with over 50 chapters across six continents fostering a vast community of more than 22,000 members highlighting that we are a locally-led, global community.

Kapil Surlaker (Vice President of Engineering AI Platform at LinkedIn) remarked the importance of thoughtful considerations for responsible AI and highlighted how the products built at LinkedIn are creating economic opportunity for every member of the global workforce.

During the Keynote  talk “Responsible AI @ Kumo AI”, Hema Raghavan (Kumo AI Co-Founder & Head of Engineering) showcased platform solutions that make machine learning on relational data simple, performant, and scalable. She emphasized the significance of Responsible AI in data models, its impact on customers and organizations, and its role from a founder’s perspective.

Key takeaways:

• Maintain transparency over the AI Decision-making process to reduce biases in the AI models
• Give data scientists control to mitigate bias and fairness issues
• Give users control to anonymize training data
• Responsible AI for customers includes security, encryption, bias and privacy

Next,  Swathi moderated the panel discussion “Building the Future of Responsible AI” packed with experts Grace Tang (Principal ML Engineer, Trust/Anti-Abuse at LinkedIn), Hema Raghavan (Co-Founder & Head of Engineering at Kumo.AI), Joaquin Quiñonero Candela (Head of Preparedness at OpenAI), and Sakshi Jain (Engineering Director, Responsible AI & Governance at LinkedIn). The discussion involved the definition of standards, the importance of addressing responsibility and transparency early in the development process in building AI solutions, biases, risks, AI principles in various scenarios, and provided career advice for fostering professional growth in the AI era.

Key takeaways:

• Humans still need to be in loop and use AI at Investment is needed for metrics which make you consider difficulties and build strong models/platforms to scale
• Building AI safety consistently: Avoid the risk of the bottom, clarify on scale. Establishing these standards ensures easy understanding through consistency
• Define standards: Inform people early in cycle and address responsibility in development. Understanding the design and ensuring compliance, including awareness of privacy and security
• Responsible AI principles: To avoid biases on people, driving products for precise recommendation, assume AI can change, and take a practical approach
• Broadness on building AI solutions: Teams with different backgrounds complement each other, and management should find out which perspective should be brought to the table
• Diversity in AI Solutions: Healthy tension stemming from diverse voices makes for better and robust AI solutions

Career advice: Play the long game, get comfortable and make AI your own. Understand how AI operates and work on a product you are passionate about.

 

The LinkedIn team talked about their AI-driven operational redesign in the talks “Infrastructure for Serving LLMs” by Tiffanny Zhou and Yingjiao (Shirley) Zhai and “Graph Neural Network Training Platform” by Shuying Liang. These were highly technical talks with some trivia sprinkled throughout to keep the audience engaged while learning AI solutions in infrastructure.

Finally, a networking session offered the chance to connect with incredible individuals and engage with the welcoming and knowledgeable speakers, WiBD volunteers, and event attendees. As part of the WiBD volunteers we provided valuable guidance on how to get involved. The event also welcomed early-career professionals, experienced data analysts and scientists, AI engineers, and those transitioning into the field.

I can’t wait for the next Women in Big Data event!