Through the looking glass

Liz will offer an overview of the approach to research performance data capture, analysis, interpretation and exploitation at the University of Melbourne, touching on analysis at multiple levels: individual, institutional, and in-between

Explaining Model Confidence Using Counterfactuals

Displaying confidence scores in human-AI interaction has been shown to help build trust between humans and AI systems. However, most existing research uses only the confidence score as a form of communication. As confidence scores are just another model output, users may want to understand why the algorithm is confident to determine whether to accept the confidence score. In this paper, we show that counterfactual explanations of confidence scores help study participants to better understand and better trust a machine learning model's prediction. We present two methods for understanding model confidence using counterfactual explanation: (1) based on counterfactual examples; and (2) based on visualisation of the counterfactual space. Both increase understanding and trust for study participants over a baseline of no explanation, but qualitative results show that they are used quite differently, leading to recommendations of when to use each one and directions of designing better explanations.Comment: AAAI 2023 Main Track. arXiv admin note: substantial text overlap with arXiv:2206.0279

Agent teamwork and reorganisation: exploring self-awareness in dynamic situations

We propose attributes that are needed in sophisticated agent teams capable of working to manage an evolving disaster. Such agent teams need to be dynamically formed and ca- pable of adaptive reorganization as the demands and com- plexity of the situation evolve. The agents need to have self- awareness of their own roles, responsibilities and capabilities and be aware of their relationships with others in the team. Each agent is not only empowered to act autonomously to- ward realizing their goals, agents are also able to negotiate to change roles as a situation changes, if reorganization is required or perceived to be in the team interest. The hierar- chical 'position' of an agent and the 'relationships' between agents govern the authority and obligations that an agent adopts. Such sophisticated agents might work in a collabora- tive team with people to self-organize and manage a critical incident such as a bush-¯re. We are planning to implement a team of agents to interface with a bush-¯re simulation, working with people in real time, to test our architecture.E