215 citations · 535 across the 22 of their papers we have counts for
4 papers · 1 filter
LEx: A Framework for Operationalising Layers of Machine Learning Explanations
Ronal Singh, Upol Ehsan, Marc Cheong +2
Several social factors impact how people respond to AI explanations used to justify AI decisions affecting them personally. In this position paper, we define a framework called the…
Deceptive Reinforcement Learning for Privacy-Preserving Planning
Zhengshang Liu, Yue Yang, Tim Miller +1
In this paper, we study the problem of deceptive reinforcement learning to preserve the privacy of a reward function. Reinforcement learning is the problem of finding a behaviour p…
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…
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…