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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.
Collaborative Human-Agent Planning for Resilience
Results show that participants' constraints improved the expected return of the plans by 10% ($p < 0. 05$) relative to baseline plans, demonstrating that human insight can be used in collaborative planning for resilience.
LEx: A Framework for Operationalising Layers of Machine Learning Explanations
Several social factors impact how people respond to AI explanations used to justify AI decisions affecting them personally.
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.
