5 papers
Project Kaleidoscope: Contextual, Human-Aligned Evaluation for Real-World AI Applications
Leanne Tan, Rohan Jaggi, Shaun Khoo +1
Evaluations (Evals) are a deployment bottleneck for real-world AI applications: public benchmarks rarely match a team's users, context, or policies, and human review is often tedio…
Small Changes, Big Impact: Demographic Bias in LLM-Based Hiring Through Subtle Sociocultural Markers in Anonymised Resumes
Bryan Chen Zhengyu Tan, Shaun Khoo, Bich Ngoc Doan +3
Large Language Models (LLMs) are increasingly deployed in resume screening pipelines. Although explicit PII (e.g., names) is commonly redacted, resumes typically retain subtle soci…
Measuring What Matters: A Framework for Evaluating Safety Risks in Real-World LLM Applications
Jia Yi Goh, Shaun Khoo, Nyx Iskandar +3
Most safety testing efforts for large language models (LLMs) today focus on evaluating foundation models. However, there is a growing need to evaluate safety at the application lev…
MinorBench: A hand-built benchmark for content-based risks for children
Shaun Khoo, Gabriel Chua, Rachel Shong
Large Language Models (LLMs) are rapidly entering children's lives - through parent-driven adoption, schools, and peer networks - yet current AI ethics and safety research do not a…
A Flexible Large Language Models Guardrail Development Methodology Applied to Off-Topic Prompt Detection
Gabriel Chua, Shing Yee Chan, Shaun Khoo
Large Language Models (LLMs) are prone to off-topic misuse, where users may prompt these models to perform tasks beyond their intended scope. Current guardrails, which often rely o…