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About

Jiaee Cheong is an emerging researcher at the intersection of human-robot interaction (HRI), affective computing, and AI ethics, with a particular focus on fairness and mental wellbeing applications. Their work addresses a critical gap in the field: the presence of machine learning bias in HRI settings, an area that had been largely overlooked despite growing attention to algorithmic fairness more broadly. Cheong's most influential contribution, "Small but Fair!" (2025, 6 citations), tackles fairness in multimodal coaching systems involving both human-human and robot-human interactions — a novel framing that bridges affective computing and HRI research communities. Complementing this, their work on multimodal gender fairness in depression prediction examines cross-cultural bias in wellbeing detection systems, drawing on data from the USA and China. The "Causal-HRI" workshop paper (2024, 4 citations) reflects Cheong's broader interest in equipping robots with deeper causal understanding of dynamic human environments. Beyond technical contributions, Cheong engages meaningfully with ethical and socio-technical dimensions of robotics deployment, exploring community perspectives on ownership and accountability in wellbeing robots. Together, these works position Cheong as a thoughtful advocate for responsible, fair, and human-centred AI in sensitive real-world contexts.