Thao Le

I am a PhD Candidate in Explainable AI (XAI) in the AI and Autonomy Lab, School of Computing and Information Systems, The University of Melbourne, under the supervision of Tim Miller (primary supervisor), Liz Sonenberg and Ronal Singh. My research interests include explainable AI, human-computer interaction, human-centered decision support and human-AI planning.

I apply mixed methods in my research by using machine learning techniques, designing/conducting behavioural experiments and doing qualitative/quantitative data analysis. My skills include (1) Python, PyTorch, Scikit-learn, Pandas, (2) machine learning/reinforcement learning techniques, (3) high performance computing (HPC), (4) building web-based applications for human-AI interaction.

My PhD research focuses on building AI-assisted decision-making systems by explaining model uncertainty and developing a more reliable decision-making model that can reduce reliance on AI. My research have been applied in various application domains, such as economics and healthcare, including supporting skin cancer diagnosis.

Selected Publications

[ECAI 2024] Towards the New XAI: A Hypothesis-Driven Approach to Decision Support Using Evidence
Thao Le, Tim Miller, Liz Sonenberg, Ronal Singh
European Conference on Artificial Intelligence (ECAI). Main Track. Acceptance Rate: 547/2,344 (23%).
Santiago de Compostela. Spain.
[Paper]

Describe and evaluate an approach for hypothesis-driven XAI based on the Weight of Evidence (WoE) framework.

Keywords: XAI, decision-making, hypothesis-driven, evidence, evaluative AI

[AAAI 2023] Explaining Model Confidence Using Counterfactuals
Thao Le, Tim Miller, Ronal Singh, Liz Sonenberg
Association for the Advancement of Artificial Intelligence (AAAI). Main Track. Acceptance Rate: 1,721/8,777 (19.6%).
Washington DC, USA.
[Paper]

Formalise counterfactual explanation of confidence and showing that this explanation can help users better understand and trust the model.

Keywords: uncertainty/confidence, counterfactual explanation, HCI

[JAIR 2021] Goal Recognition for Deceptive Human Agents through Planning and Gaze
Thao Le, Ronal Singh, Tim Miller
Journal of Artificial Intelligence Research. IJCAI 2022 Journal Track.
[Paper]

Present new models for goal recognition under deception using a combination of gaze behaviour and observed movements of the agent.

Keywords: planning, HCI

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