5 citations · 5 across the 2 of their papers we have counts for
6 papers
Directive Explanations for Actionable Explainability in Machine Learning Applications
Ronal Singh, Paul Dourish, Piers Howe +4
This paper investigates the prospects of using directive explanations to assist people in achieving recourse of machine learning decisions. Directive explanations list which specif…
Distal Explanations for Model-free Explainable Reinforcement Learning
Prashan Madumal, Tim Miller, Liz Sonenberg +1
In this paper we introduce and evaluate a distal explanation model for model-free reinforcement learning agents that can generate explanations for `why' and `why not' questions. Ou…
Explainable Reinforcement Learning Through a Causal Lens
Prashan Madumal, Tim Miller, Liz Sonenberg +1
Prevalent theories in cognitive science propose that humans understand and represent the knowledge of the world through causal relationships. In making sense of the world, we build…
A Grounded Interaction Protocol for Explainable Artificial Intelligence
Prashan Madumal, Tim Miller, Liz Sonenberg +1
Explainable Artificial Intelligence (XAI) systems need to include an explanation model to communicate the internal decisions, behaviours and actions to the interacting humans. Succ…
Interaction Design for Explainable AI: Workshop Proceedings
Prashan Madumal, Ronal Singh, Joshua Newn +1
As artificial intelligence (AI) systems become increasingly complex and ubiquitous, these systems will be responsible for making decisions that directly affect individuals and soci…
Towards a Grounded Dialog Model for Explainable Artificial Intelligence
Prashan Madumal, Tim Miller, Frank Vetere +1
To generate trust with their users, Explainable Artificial Intelligence (XAI) systems need to include an explanation model that can communicate the internal decisions, behaviours a…