4 citations · 5 across the 16 of their papers we have counts for
16 papers
Investigating Associational Biases in Inter-Model Communication of Large Generative Models
Fethiye Irmak Dogan, Yuval Weiss, Kajal Patel +2
Social bias in generative AI can manifest not only as performance disparities but also as associational bias, whereby models learn and reproduce stereotypical associations between…
EQPO: Equitable Group Relative Policy Optimization for Clinical Reasoning
Shiqi Dai, Wei Dai, Jiaee Cheong +1
Medical AI systems demonstrated impressive diagnostic performance, yet they routinely show uneven accuracy across demographic groups, disadvantaging underrepresented populations. A…
Human Behavior Atlas: Benchmarking Unified Psychological and Social Behavior Understanding
Keane Ong, Wei Dai, Carol Li +8
Using intelligent systems to perceive psychological and social behaviors, that is, the underlying affective, cognitive, and pathological states that are manifested through observab…
MindBenchAI: An Actionable Platform to Evaluate the Profile and Performance of Large Language Models in a Mental Healthcare Context
Bridget Dwyer, Matthew Flathers, Akane Sano +28
Individuals are increasingly utilizing large language model (LLM)based tools for mental health guidance and crisis support in place of human experts. While AI technology has great…
Who Owns The Robot?: Four Ethical and Socio-technical Questions about Wellbeing Robots in the Real World through Community Engagement
Minja Axelsson, Jiaee Cheong, Rune Nyrup +1
Recent studies indicate that robotic coaches can play a crucial role in promoting wellbeing. However, the real-world deployment of wellbeing robots raises numerous ethical and soci…
FairSSL: Fair Multimodal Self-Supervised Learning
Jiaee Cheong, Abtin Mogharabin, Paul Liang +2
Prevalent multimodal self-supervised learning (SSL) methods rely on the redundancy assumption: that different views share substantial task-relevant information. We argue that this…
Automatic Depression Assessment using Machine Learning: A Comprehensive Survey
Siyang Song, Yupeng Huo, Shiqing Tang +4
Depression is a common mental illness across current human society. Traditional depression assessment relying on inventories and interviews with psychologists frequently suffer fro…
Gender Fairness of Machine Learning Algorithms for Pain Detection
Dylan Green, Yuting Shang, Jiaee Cheong +2
Automated pain detection through machine learning (ML) and deep learning (DL) algorithms holds significant potential in healthcare, particularly for patients unable to self-report…
FG 2025 TrustFAA: the First Workshop on Towards Trustworthy Facial Affect Analysis: Advancing Insights of Fairness, Explainability, and Safety (TrustFAA)
Jiaee Cheong, Yang Liu, Harold Soh +1
With the increasing prevalence and deployment of Emotion AI-powered facial affect analysis (FAA) tools, concerns about the trustworthiness of these systems have become more promine…
Exploring Causality for HRI: A Case Study on Robotic Mental Well-being Coaching
Micol Spitale, Srikar Babu, Serhan Cakmak +2
One of the primary goals of Human-Robot Interaction (HRI) research is to develop robots that can interpret human behavior and adapt their responses accordingly. Adaptive learning m…
Machine Learning Fairness for Depression Detection using EEG Data
Angus Man Ho Kwok, Jiaee Cheong, Sinan Kalkan +1
This paper presents the very first attempt to evaluate machine learning fairness for depression detection using electroencephalogram (EEG) data. We conduct experiments using differ…
U-Fair: Uncertainty-based Multimodal Multitask Learning for Fairer Depression Detection
Jiaee Cheong, Aditya Bangar, Sinan Kalkan +1
Machine learning bias in mental health is becoming an increasingly pertinent challenge. Despite promising efforts indicating that multitask approaches often work better than unitas…
Multimodal Gender Fairness in Depression Prediction: Insights on Data from the USA & China
Joseph Cameron, Jiaee Cheong, Micol Spitale +1
Social agents and robots are increasingly being used in wellbeing settings. However, a key challenge is that these agents and robots typically rely on machine learning (ML) algorit…
Small but Fair! Fairness for Multimodal Human-Human and Robot-Human Mental Wellbeing Coaching
Jiaee Cheong, Micol Spitale, Hatice Gunes
In recent years, the affective computing (AC) and human-robot interaction (HRI) research communities have put fairness at the centre of their research agenda. However, none of the…
Underneath the Numbers: Quantitative and Qualitative Gender Fairness in LLMs for Depression Prediction
Micol Spitale, Jiaee Cheong, Hatice Gunes
Recent studies show bias in many machine learning models for depression detection, but bias in LLMs for this task remains unexplored. This work presents the first attempt to invest…
Uncertainty-based Fairness Measures
Selim Kuzucu, Jiaee Cheong, Hatice Gunes +1
Unfair predictions of machine learning (ML) models impede their broad acceptance in real-world settings. Tackling this arduous challenge first necessitates defining what it means f…