Research
We aim to address the complex and multifaceted issues surrounding the intersection of technology and ethics. As computing continues to transform nearly every aspect of our lives, it is critical to consider the ethical implications of its development and use. We are dedicated to exploring a range of topics, from bias to fairness, sustainability, and digital responsibility. Through our work, we strive to promote a more just and equitable digital future for all.
Research Project: Regional Bias in Monolingual English Language Models
This study explores whether English language models (LLMs) contain biases towards specific regions, and how these biases affect their performance in tasks like Natural Language Processing (NLP). By looking into subtle differences in word meanings across regions,...
Research Project: Machine Learning to Predict Severe Acute Pancreatitis
Supervised by Professor Gill Dobbie, Dr Vithya Yogarajan, and Professor John Windsor (Faculty of Medical and Health Sciences, the University of Auckland), this project seeks to leverage machine learning for healthcare. Title: Machine Learning to Predict Severe Acute...
Research Project: How do we know those Artificial Intelligence models are fair? An overview of bias evaluating frameworks for AI models
Under the supervision of Professor Gill Dobbie and Dr Vithya Yogarajan, this research project by Kejun Dai reviews frameworks for evaluating bias in artificial intelligence models. Situated within the broader "Ethical Computing" initiative led by Professor Gill...
Research Seminar: Challenges in Annotating Datasets to Quantify Bias
Gill delivered a presentation at the ARC Training Centre in Data Analytics for Resources and Environments (DARE), The University of Sydney, where she shared insights from her research focused on quantifying bias in language models with the ultimate goal of debiasing....
Research Paper: Developing a Fair AI-based Healthcare Framework with Feedback Loop
Yogarajan, V., Dobbie, G., Leitch, S., & Reith, D. (2023). Developing a Fair AI-based Healthcare Framework with Feedback Loop. The 6th international workshop on Knowledge Discovery in Healthcare Data (KDH). Abstract Artificial intelligence (AI) driven...
Green Computing Hub
To foster collaboration and support the exchange of ideas and solutions on sustainable computing, we establish an international green computing hub. In recent years, the energy consumption that is needed for computing processes has become an increasing concern....
Research Paper: Identifying potential patients with diabetes-related dementia: a descriptive approach using routinely collected data
(Image Source: Rens Dimmendaal & Johann Siemens / Better Images of AI / Decision Tree reversed / CC-BY 4.0) Cristian Gonzalez Prieto, Ruby Hosking, Jasmine Appleton, Susan Yates, Yu-Min Lin, Bede Oulaghan, Claudia Rivera-Rodriguez, Daniel Wilson, Gillian...
Identifying and Developing Human Imitation Learners in Non-Zero-Sum Environments
Deep reinforcement learning has become increasingly relevant in fields such as recommendation systems, finance, and healthcare. While games have been a popular area of study in deep reinforcement learning due to their clearly defined spaces that fit within Markov...
Research Paper: Effectiveness of Debiasing Techniques: An Indigenous Qualitative Analysis
V. Yogarajan, G. Dobbie, and H. Gouk, “Effectiveness of Debiasing Techniques: An Indigenous Qualitative Analysis,” ICLR Tinypapers, 2023. doi: 10.48550/arXiv.2304.11094. An indigenous perspective on the effectiveness of debiasing techniques for pre-trained...
Poster: Exploring older people’s attitudes and preferences around the use of their routinely collected healthcare data in brain health research in New Zealand
In the poster, we will present the results of a survey conducted with people aged 55 and over currently engaged with health services in Te Whatu Ora Counties Manukau. We explored those people's attitudes and preferences about using their health data in research,...