As AI becomes more ubiquitous, complex and consequential, the need for people to understand how decisions are made and to judge their correctness, fairness, and transparency, becomes increasingly crucial due to concerns of ethics and trust.
The field of Explainable AI (XAI), aims to address this problem by designing intelligent agents whose decisions and actions building up to these decisions can be easily understood by humans.
Truly explainable AI requires integration of the technical and human challenges. To make progress, we need a sophisticated understanding of what constitutes “explanation” in an AI context; one that is not merely limited to an articulation of the software processes, but explicitly considers that it is ultimately people that will consume these explanations.
The research into explainable AI in our research laboratory at the University of Melbourne is aiming to build explainable AI from these very principles.
We aim to take a human-centered approach to XAI, explicitly studying what questions people care about for explanation, what makes a good explanation to a person, how explanations and causes can be extracted from complex and often opaque decision-making models, and how they can be communicated to people.
This research effort is a collaborative project involving computer science, cognitive science, social psychology, and human-computer interaction, treating explainable AI as an interaction between person and machine.
The project is lead by Associate Professor Timothy Miller.
Relevant research collaborations include Associate Professor Piers Howe from the Melbourne School of Psychology.
Miller, Tim, Piers Howe, and Liz Sonenberg. “Explainable AI: Beware of Inmates Running the Asylum Or: How I Learnt to Stop Worrying and Love the Social and Behavioural Sciences.” arXiv preprint arXiv:1712.00547 (2017).
Miller, Tim. “Explanation in Artificial Intelligence: Insights from the Social Sciences.” arXiv preprint arXiv:1706.07269 (2017).
Defence Science and Technology Group Microsoft Research