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Article
Computer Science, Artificial Intelligence
Ghada El-khawaga et al.
Summary: This paper introduces a framework that analyzes the impact of PPM-related settings and ML-model-related choices on the characteristics and expressiveness of the generated explanations. The framework provides a means to examine explanations for the reasoning process or predictions made in a certain business process instance. It also allows for comparison of different characteristics of explainability methods and their impact on the underlying model reasoning process.
Article
Computer Science, Information Systems
Jens Brunk et al.
Summary: This paper explores predicting undesirable events during the execution of a business process instance by leveraging a Dynamic Bayesian Network technique that incorporates context attributes. Evaluation with real-life data sets shows superior prediction results when context information is correctly introduced into the model. The approach considers a multi-perspective view, combining the flow perspective of the process with its surrounding context.
INFORMATION SYSTEMS
(2021)
Review
Computer Science, Information Systems
Jianlong Zhou et al.
Summary: The paper provides a comprehensive overview of methods proposed for the evaluation of ML explanations in the current literature. It identifies properties of explainability from the review of definitions of explainability and uses them as objectives that evaluation metrics should achieve. The survey found that different explanation methods use quantitative metrics primarily to evaluate either simplicity of interpretability or fidelity of explainability, while subjective measures like trust and confidence are key in human-centered evaluation of explainable systems.
Article
Computer Science, Artificial Intelligence
Sina Mohseni et al.
Summary: The demand for interpretable and accountable intelligent systems is increasing as artificial intelligence applications become more prevalent in everyday life. Researchers from various disciplines collaborate to define, design, and assess explainable AI systems. By categorizing XAI design goals and evaluation methods, this article aims to support different design objectives and evaluation methods in interdisciplinary XAI research.
ACM TRANSACTIONS ON INTERACTIVE INTELLIGENT SYSTEMS
(2021)
Proceedings Paper
Computer Science, Artificial Intelligence
Vincenzo Pasquadibisceglie et al.
Summary: PPM techniques are crucial for operational support in organizations, with recent emphasis on the interpretability of predictive models and the emerging concept of XAI. This paper introduces a fully interpretable outcome prediction model based on fuzzy rules acquired via training a neuro-fuzzy network, striking a balance between predictive accuracy and interpretability. Encouraging experimental results on benchmark event logs underline the importance of developing explainable models for predictive process analytics.
2021 3RD INTERNATIONAL CONFERENCE ON PROCESS MINING (ICPM 2021)
(2021)
Review
Computer Science, Information Systems
Vaishak Belle et al.
Summary: Artificial intelligence offers opportunities for improving private and public life, but understanding the decisions made by AI systems to trust them poses a significant challenge. This report focuses on data-driven methods, particularly machine learning and pattern recognition models, to survey and distill results from literature. It aims to help industry practitioners better understand the field of explainable machine learning and apply the right tools.
FRONTIERS IN BIG DATA
(2021)
Article
Computer Science, Artificial Intelligence
Alejandro Barredo Arrieta et al.
INFORMATION FUSION
(2020)
Article
Statistics & Probability
Daniel W. Apley et al.
JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY
(2020)
Proceedings Paper
Computer Science, Artificial Intelligence
Riccardo Galanti et al.
2020 2ND INTERNATIONAL CONFERENCE ON PROCESS MINING (ICPM 2020)
(2020)
Article
Computer Science, Information Systems
Chiara Di Francescomarino et al.
IEEE TRANSACTIONS ON SERVICES COMPUTING
(2019)
Review
Computer Science, Information Systems
Irene Teinemaa et al.
ACM TRANSACTIONS ON KNOWLEDGE DISCOVERY FROM DATA
(2019)
Article
Computer Science, Software Engineering
Ilya Verenich et al.
JOURNAL OF SOFTWARE-EVOLUTION AND PROCESS
(2019)
Article
Computer Science, Artificial Intelligence
Ilya Verenich et al.
ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY
(2019)
Article
Computer Science, Information Systems
Alfonso Eduardo Marquez-Chamorro et al.
IEEE TRANSACTIONS ON SERVICES COMPUTING
(2018)
Article
Statistics & Probability
JH Friedman
ANNALS OF STATISTICS
(2001)
