The One About Recruitment and Recommenders
How do you build AI systems that match people to opportunities fairly and effectively? From bias detection in hiring tools to LLM-powered recommender systems, explore how AI is reshaping recruitment processes, the complexities of building fair hiring systems, and technical advances in recommendation algorithms.
More About the Sharings
Kevin (CEO, Fuku AI) will share how AI is fundamentally reshaping human resources. From intelligent CV matching and AI-powered video interviews analyzing facial expressions and vocal patterns, to predictive models forecasting candidate performance and career potential, discover the evolution toward full-process intelligence and personalized candidate experiences. Learn about the emerging human-AI collaboration model where technology handles repetitive tasks while HR professionals focus on strategic relationship-building and workforce planning. (Technical Level: 100)
Shaun (Technical Lead, GovTech) will share more about "Hiring without Harming: Fairness and Bias in Using AI for Recruitment". Hear about how bias seeps into AI hiring systems and discover why removing sensitive data doesn't solve the problem. He will also talk about the complexities of measuring fairness and explore different approaches for creating fairer AI-assisted hiring processes. (Technical Level: 100)
Dr Sun Zhu (Assistant Professor, SUTD) will share on how generative models, especially LLMs, can be used in the context of recommender systems, spanning from (1) leveraging LLMs to extract features to enhance traditional deep learning recommenders, (2) exploring the in-context learning capabilities of LLMs for recommendation, including automatic prompt optimization, dynamic customized few-shot example generation, and reinforcement learning-enhanced prompts, and (3) exploiting parameter-efficient fine-tuning techniques to inject domain-specific knowledge into LLMs for more accurate recommendations. Finally, the talk will discuss other interesting and promising directions within this research area. (Technical Level: 200)
More About the Speakers
Kevin Gao is the Founder & CEO of Fuku AI, a Singapore-based HR tech startup transforming recruitment for SMEs and agencies across Asia with artificial intelligence. Kevin has 15 years recruitment experience serving clients in legal, technology, life sciences, and financial sectors. Having witnessed first-hand the inefficiencies in hiring — from writing job descriptions to screening mountains of CVs — he founded Fuku AI to make hiring faster and smarter. At Fuku AI, Kevin is driving the development of an end-to-end AI recruitment platform that helps businesses generate job descriptions, automate CV screening and candidate ranking, conduct AI-powered interviews.
Shaun Khoo is a technical lead for the Responsible AI team within GovTech's AI Practice, specifically focusing on AI safety, robustness, and fairness. Their previous work includes developing the first version of LionGuard, a Singapore-contextualised moderation guardrail, and conducting fairness assessments for an internal GenAI tool. Before this, Shaun was a data science team lead deployed to the Ministry of Manpower, and graduated from Columbia University and Oxford University.
Dr Sun Zhu is currently an Assistant Professor at the Information Systems Technology and Design Pillar, Singapore University of Technology and Design. She received her Ph.D. degree from Nanyang Technological University, Singapore. Her main research topic is AI, specializing in trustworthy recommender systems. She has published papers in many leading conferences and journals, including SIGIR, SIGKDD, NeurIPS, IJCAI, AAAI, CIKM, RecSys, TPAMI, TKDE, and TNNLS. She is the AE with Electronic Commerce Research and Applications (ECRA) and ACM Transactions on Recommender Systems (TORS), and PC/Senior PC Member for KDD, SIGIR, IJCAI, AAAI, SDM, CIKM, RecSys, IUI, etc.
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