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Found 13 papers, 5 papers with code
Generating Feasible and Plausible Counterfactual Explanations for Outcome Prediction of Business Processes
In recent years, various machine and deep learning architectures have been successfully introduced to the field of predictive process analytics.
MDF-Net for abnormality detection by fusing X-rays with clinical data
In this work, we propose a novel architecture consisting of two fusion methods that enable the model to simultaneously process patients' clinical data (structured data) and chest X-rays (image data).
Benchmarking Counterfactual Algorithms for XAI: From White Box to Black Box
This study investigates the impact of machine learning models on the generation of counterfactual explanations by conducting a benchmark evaluation over three different types of models: decision-tree (fully transparent, interpretable, white-box model), a random forest (a semi-interpretable, grey-box model), and a neural network (a fully opaque, black-box model).
Building Interpretable Models for Business Process Prediction using Shared and Specialised Attention Mechanisms
In this paper, we address the "black-box" problem in predictive process analytics by building interpretable models that are capable to inform both what and why is a prediction.
DiCE4EL: Interpreting Process Predictions using a Milestone-Aware Counterfactual Approach
We design an extension of DiCE, namely DiCE4EL (DiCE for Event Logs), that can generate counterfactual explanations for process prediction, and propose an approach that supports deriving milestone-aware counterfactual explanations at key intermediate stages along process execution to promote interpretability.
Explainable AI Enabled Inspection of Business Process Prediction Models
As an important branch of state-of-the-art data analytics, business process predictions are also faced with a challenge in regard to the lack of explanation to the reasoning and decision by the underlying `black-box' prediction models.
Developing a Fidelity Evaluation Approach for Interpretable Machine Learning
Although modern machine learning and deep learning methods allow for complex and in-depth data analytics, the predictive models generated by these methods are often highly complex, and lack transparency.
BIG-bench Machine Learning
Explainable Artificial Intelligence (XAI)
+2
Counterfactuals and Causability in Explainable Artificial Intelligence: Theory, Algorithms, and Applications
This paper presents an in-depth systematic review of the diverse existing body of literature on counterfactuals and causability for explainable artificial intelligence.
Evaluating Explainable Methods for Predictive Process Analytics: A Functionally-Grounded Approach
Current explainable machine learning methods, such as LIME and SHAP, can be used to interpret black box models.
OrgMining 2.0: A Novel Framework for Organizational Model Mining from Event Logs
We demonstrate the feasibility of this framework by proposing an approach underpinned by the framework for organizational model discovery, and also conduct experiments on real-life event logs to discover and evaluate organizational models.
An Interpretable Probabilistic Approach for Demystifying Black-box Predictive Models
This has led to an increased interest in interpretable machine learning, where post hoc interpretation presents a useful mechanism for generating interpretations of complex learning models.
An Investigation of Interpretability Techniques for Deep Learning in Predictive Process Analytics
We see certain distinct features used for predictions that provide useful insights about the type of cancer, along with features that do not generalize well.
Decision Making
Interpretability Techniques for Deep Learning
Exploring Interpretability for Predictive Process Analytics
The explanations allow us to gain an understanding of the underlying reasons for a prediction and highlight scenarios where accuracy alone may not be sufficient in assessing the suitability of techniques used to encode event log data to features used by a predictive model.
