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Found 11 papers, 1 papers with code
Towards the New XAI: A Hypothesis-Driven Approach to Decision Support Using Evidence
Prior research on AI-assisted human decision-making has explored several different explainable AI (XAI) approaches.
Explaining Model Confidence Using Counterfactuals
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.
Improving Model Understanding and Trust with Counterfactual Explanations of Model Confidence
In this paper, we show that counterfactual explanations of confidence scores help users better understand and better trust an AI model's prediction in human-subject studies.
Efficient Multi-agent Epistemic Planning: Teaching Planners About Nested Belief
Many AI applications involve the interaction of multiple autonomous agents, requiring those agents to reason about their own beliefs, as well as those of other agents.
Directive Explanations for Actionable Explainability in Machine Learning Applications
This paper investigates the prospects of using directive explanations to assist people in achieving recourse of machine learning decisions.
Distal Explanations for Model-free Explainable Reinforcement Learning
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.
Explainable Reinforcement Learning Through a Causal Lens
In this paper, we use causal models to derive causal explanations of behaviour of reinforcement learning agents.
A Grounded Interaction Protocol for Explainable Artificial Intelligence
Explainable Artificial Intelligence (XAI) systems need to include an explanation model to communicate the internal decisions, behaviours and actions to the interacting humans.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
Towards a Grounded Dialog Model for Explainable Artificial Intelligence
We carry out further analysis to identify the relationships between components and sequences and cycles that occur in a dialog.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
Explainable AI: Beware of Inmates Running the Asylum Or: How I Learnt to Stop Worrying and Love the Social and Behavioural Sciences
As a result, programmers design software for themselves, rather than for their target audience, a phenomenon he refers to as the `inmates running the asylum'.
Social planning for social HRI
Making a computational agent 'social' has implications for how it perceives itself and the environment in which it is situated, including the ability to recognise the behaviours of others.
