[AISWorld] Call for Papers: Annals of OR Special Issue

[Bajaj, Akhilesh]

Call for Papers

Annals of Operations Research

Special Issue: Interpretable Machine Learning and Explainable

Artificial Intelligence

Closing Date: March 31, 2023

_______________________________________________________________________

Annals of Operations Research seeks submissions for a special issue on Interpretable Machine Learning and Explainable Artificial Intelligence. The deadline for submission is March 31, 2023.

Machine learning (ML) has garnered significant interest in recent years due to its applicability to a wide range of complex problems. There is increasing realization that ML models, in addition to making predictions, reveal information about relationships between domain data items, commonly referred to as interpretability of the model. A similar situation is occurring in the artificial intelligence (AI) scientific community, which has concentrated on explainable AI (XAI) along the dimensions of algorithmic interpretability, explainability, transparency, and accountability of algorithmic judgments. ML approaches may be classified as white-box or black-box. White-box techniques, like rule learners and inductive logic programming, provide explicit models that are intrinsically interpretable, while black-box techniques, such as (deep) neural networks, provide opaque models. With the growing use of ML, there have been significant social concerns about implementing black-box models for decisions requiring the explanation of domain relationships. The ability to express information obtained from ML models in human-comprehensible language-aka interpretability-has sparked considerable attention in academics and industry. These interpretations have found applications in healthcare, transportation, finance, education, policymaking, criminal justice, etc. As it evolves, one aim in ML is the development of interpretable techniques and models that explain themselves and their output.

This special issue invites papers on advancements in interpretable ML from the modeling and learning perspectives. We are looking for high-quality, original articles presenting work on the following (not exhaustive) topics:

* Probabilistic graphical model applications

* Explainable artificial intelligence

* Rule learning for interpretable machine learning

* Interpretation of black-box models

* Interpretability in reinforcement learning

* Interpretable supervised and unsupervised models





* Interpretation of neural networks and ensemble-based methods

* Interpretation of random forests and other ensemble models

* Causality of machine learning models

* Novel applications requiring interpretability

* Methodologies for measuring interpretability of machine learning models

* Interpretability-accuracy trade-off and its benchmarks



Instructions for authors can be found at: https://www.springer.com/journal/10479/submission-guidelines

Authors should submit a cover letter and a manuscript by March 31, 2023, via the Journal's online submission site. Please see the Author Instructions on the web site if you have not yet submitted a paper through Springer's web-based system, Editorial Manager. When prompted for the article type, please select Original Research. On the Additional Information screen, you will be asked if the manuscript belongs to a special issue, please choose yes and the special issue's title, Interpretable Machine Learning and Explainable Artificial Intelligence, to ensure that it will be reviewed for this special issue. Manuscripts submitted after the deadline may not be considered for the special issue and may be transferred, if accepted, to a regular issue.

Papers will be subject to a strict review process under the supervision of the Guest Editors, and accepted papers will be published online individually, before print publication.

Special Issue Guest Editors

Kazim Topuz

Chapman Assistant Professor of Operations Management & Business Analytics Finance and Operations Management, The University of Tulsa

kazim-topuz at utulsa.edu

Akhilesh Bajaj

Professor of CIS, The University of Tulsa akhilesh-bajaj at utulsa.edu

Kristof Coussement

Professor of Business Analytics, IESEG School of Management k.coussement at ieseg.fr

Timothy L. Urban
Professor Emeritus of Operations Management timothy-urban at utulsa.edu

Akhilesh Bajaj
Professor of CIS
Chapman Endowed Chair of CIS
Director, School of Accounting and CIS
The University of Tulsa
W: http://abajaj.net<http://abajaj.net/>


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