Human performance effects of combining counterfactual explanations with normative and contrastive explanations in supervised machine learning for automated decision assistance

Abstract

• Counterfactual explanations gained popularity as a solution for aiding causal understanding and explain the reasons behind machine learning outputs. • Empirical data on the influence of counterfactuals on human decisions is scarce compared to traditional explanation methods. • An experiment analyzed the effects of counterfactual explanations alongside normative and contrastive explanations in condition-based maintenance. • Including counterfactual explanations reduced false alarm rates and potentially decreased decision time and workload. • Caution is advised against overstating the benefits of counterfactuals in digital work environments. Counterfactual explanations have emerged as a popular solution for elucidating the reasons behind machine learning predictions due to their contribution in supporting people's understanding of causality. Despite psychological research suggesting potential burdens associated with counterfactuals, empirical data on the influence of counterfactual explanations on human decisions is limited, especially in comparison with other more traditional explanation methods in machine learning for decision assistance. We present an experiment to examine the human performance effects of counterfactual explanations combined with normative and contrastive explanations in the context of condition-based manteinance. Twenty-four participants provided their diagnosis of the conditions of a hydraulic system with the assistance of a simulated decision aid based on machine learning, under four experimental conditions (baseline with no explanations, normative plus contrastive explanations, normative plus counterfactual explanations, and normative plus contrastive plus counterfactual explanations). The results indicate a lack of significant performance differences between explanation conditions. However, we found a reduction in false alarm rate in the condition with all three explanations, and a potential reduction in decision time and workload in the two conditions that included counterfactual explanations. These findings highlight the potential of counterfactuals to reduce decision time and workload, but they also caution against overestimating their benefits in supporting decision performance within digital work environments.

Topics & Concepts