The Role of Explanation Styles and Perceived Accuracy on Decision Making in Predictive Process Monitoring

• URL: http://arxiv.org/abs/2506.16617v1
• Date: Thu, 19 Jun 2025 21:30:28 GMT
• Title: The Role of Explanation Styles and Perceived Accuracy on Decision Making in Predictive Process Monitoring
• Authors: Soobin Chae, Suhwan Lee, Hanna Hauptmann, Hajo A. Reijers, Xixi Lu,
• Abstract summary: Explainable AI (XAI) aims to address this challenge by providing the reasoning behind predictions.<n>Current evaluations of XAI in Predictive Process Monitoring (PPM) focus primarily on functional metrics, overlooking user-centered aspects.<n>This study investigates the effects of explanation styles (feature importance, rule-based, and counterfactual) and perceived AI accuracy (low or high) on decision-making.
• Score: 1.2808136856881935
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Predictive Process Monitoring (PPM) often uses deep learning models to predict the future behavior of ongoing processes, such as predicting process outcomes. While these models achieve high accuracy, their lack of interpretability undermines user trust and adoption. Explainable AI (XAI) aims to address this challenge by providing the reasoning behind the predictions. However, current evaluations of XAI in PPM focus primarily on functional metrics (such as fidelity), overlooking user-centered aspects such as their effect on task performance and decision-making. This study investigates the effects of explanation styles (feature importance, rule-based, and counterfactual) and perceived AI accuracy (low or high) on decision-making in PPM. We conducted a decision-making experiment, where users were presented with the AI predictions, perceived accuracy levels, and explanations of different styles. Users' decisions were measured both before and after receiving explanations, allowing the assessment of objective metrics (Task Performance and Agreement) and subjective metrics (Decision Confidence). Our findings show that perceived accuracy and explanation style have a significant effect.

Related papers

• Evaluating Interventional Reasoning Capabilities of Large Language Models [58.52919374786108]
  Large language models (LLMs) are used to automate decision-making tasks.<n>In this paper, we evaluate whether LLMs can accurately update their knowledge of a data-generating process in response to an intervention.<n>We create benchmarks that span diverse causal graphs (e.g., confounding, mediation) and variable types.<n>These benchmarks allow us to isolate the ability of LLMs to accurately predict changes resulting from their ability to memorize facts or find other shortcuts.
  arXiv  Detail & Related papers  (2024-04-08T14:15:56Z)
• Robust Design and Evaluation of Predictive Algorithms under Unobserved Confounding [2.8498944632323755]
  We propose a unified framework for the robust design and evaluation of predictive algorithms in selectively observed data. We impose general assumptions on how much the outcome may vary on average between unselected and selected units. We develop debiased machine learning estimators for the bounds on a large class of predictive performance estimands.
  arXiv  Detail & Related papers  (2022-12-19T20:41:44Z)
• Explainability's Gain is Optimality's Loss? -- How Explanations Bias Decision-making [0.0]
  Explanations help to facilitate communication between the algorithm and the human decision-maker. Feature-based explanations' semantics of causal models induce leakage from the decision-maker's prior beliefs. Such differences can lead to sub-optimal and biased decision outcomes.
  arXiv  Detail & Related papers  (2022-06-17T11:43:42Z)
• What Should I Know? Using Meta-gradient Descent for Predictive Feature Discovery in a Single Stream of Experience [63.75363908696257]
  computational reinforcement learning seeks to construct an agent's perception of the world through predictions of future sensations. An open challenge in this line of work is determining from the infinitely many predictions that the agent could possibly make which predictions might best support decision-making. We introduce a meta-gradient descent process by which an agent learns what predictions to make, 2) the estimates for its chosen predictions, and 3) how to use those estimates to generate policies that maximize future reward.
  arXiv  Detail & Related papers  (2022-06-13T21:31:06Z)
• Predicting and Understanding Human Action Decisions during Skillful Joint-Action via Machine Learning and Explainable-AI [1.3381749415517021]
  This study uses supervised machine learning and explainable artificial intelligence to model, predict and understand human decision-making. Long short-term memory networks were trained to predict the target selection decisions of expert and novice actors completing a dyadic herding task.
  arXiv  Detail & Related papers  (2022-06-06T16:54:43Z)
• Explainability in Process Outcome Prediction: Guidelines to Obtain Interpretable and Faithful Models [77.34726150561087]
  We define explainability through the interpretability of the explanations and the faithfulness of the explainability model in the field of process outcome prediction. This paper contributes a set of guidelines named X-MOP which allows selecting the appropriate model based on the event log specifications.
  arXiv  Detail & Related papers  (2022-03-30T05:59:50Z)
• Inverse Online Learning: Understanding Non-Stationary and Reactionary Policies [79.60322329952453]
  We show how to develop interpretable representations of how agents make decisions. By understanding the decision-making processes underlying a set of observed trajectories, we cast the policy inference problem as the inverse to this online learning problem. We introduce a practical algorithm for retrospectively estimating such perceived effects, alongside the process through which agents update them. Through application to the analysis of UNOS organ donation acceptance decisions, we demonstrate that our approach can bring valuable insights into the factors that govern decision processes and how they change over time.
  arXiv  Detail & Related papers  (2022-03-14T17:40:42Z)
• On the Fairness of Machine-Assisted Human Decisions [3.4069627091757178]
  We show that the inclusion of a biased human decision-maker can revert common relationships between the structure of the algorithm and the qualities of resulting decisions. In the lab experiment, we demonstrate how predictions informed by gender-specific information can reduce average gender disparities in decisions.
  arXiv  Detail & Related papers  (2021-10-28T17:24:45Z)
• Evaluating Explainable Methods for Predictive Process Analytics: A Functionally-Grounded Approach [2.2448567386846916]
  Predictive process analytics focuses on predicting the future states of running instances of a business process. Current explainable machine learning methods, such as LIME and SHAP, can be used to interpret black box models. We apply the proposed metrics to evaluate the performance of LIME and SHAP in interpreting process predictive models built on XGBoost.
  arXiv  Detail & Related papers  (2020-12-08T05:05:19Z)
• When Does Uncertainty Matter?: Understanding the Impact of Predictive Uncertainty in ML Assisted Decision Making [68.19284302320146]
  We carry out user studies to assess how people with differing levels of expertise respond to different types of predictive uncertainty. We found that showing posterior predictive distributions led to smaller disagreements with the ML model's predictions. This suggests that posterior predictive distributions can potentially serve as useful decision aids which should be used with caution and take into account the type of distribution and the expertise of the human.
  arXiv  Detail & Related papers  (2020-11-12T02:23:53Z)
• Counterfactual Predictions under Runtime Confounding [74.90756694584839]
  We study the counterfactual prediction task in the setting where all relevant factors are captured in the historical data. We propose a doubly-robust procedure for learning counterfactual prediction models in this setting.
  arXiv  Detail & Related papers  (2020-06-30T15:49:05Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.