International Workshop on Adaptable, Reliable, and Responsible Learning(ARRL)

Co-located with IEEE ICDM 2023.

Half-day on December 4th, 2023, Shanghai,China

For years, machine Learning has advanced artificial intelligence (AI) by enabling the development of systems that generate models from various databases without explicit instruction. The growing availability of data across various fields has led to the proliferation of learning-enabled systems, which embed machine learning components in the core, that have become increasingly powerful and integral to industry and everyday life. Data mining techniques allow such systems to examine vast quantities of data, identifying subtle features that often elude human capabilities. However, these techniques frequently rely on oversimplified learning objectives and data that may be biased, incomplete, or even hazardous. The transition from learning-enabled systems into real-world decision-making contexts thus can pose risks, primarily due to their limited adaptability, reliability, and responsibility in dealing with unfamiliar or unknown circumstances.

The inaugural International Workshop on Adaptable, Reliable, and Responsible Learning (ARRL) aims to gather researchers and practitioners to present recent advancements in addressing the three key aspects of learning within the context of data-driven and data-centric systems: adaptability, reliability, and responsibility. The workshop will explore theoretical foundations, algorithm designs, and frameworks that ensure future learning-enabled systems are

1) *Adaptable*, by exhibiting evolvability with changes in the environment, societal dynamics, and task objectives or requirements, ensuring that the system remains relevant and effective in addressing diverse and dynamic challenges while maintaining high-performance standards;

2) *Reliable*, by demonstrating robustness and stability in the presence of uncertainty, variability, and unknown unknowns, ensuring system safety and performance consistency across diverse conditions and high-stakes operating environments; and

3) *Responsible*, by promoting sustainability, fairness, explainability, and trustworthiness in learning processes and outcomes, addressing ethical and privacy concerns and championing technology use for positive societal impact including solutions for affordable clean energy and climate action.

This workshop cordially invites submissions that showcase cutting-edge advances in research and development of adaptable, reliable, and responsible (ARR) learning algorithms and designs, as well as late-breaking research that introduces published work or software that address ARR challenges and provide significant value to the community.


July 1st, 2023 Paper Submission
September 1st, 2023 Author Notification
October 15th, 2023 Camera-Ready
October 15th, 2023 Registration
December 1 -- 4th, 2023 Conference Date


Theory, methodology, and resource papers are welcome from any of the following areas, including but not limited to:

Adaptable Learning Reliable Learning Responsible Learning


    Paper submission link: International Workshop on Adaptable, Reliable, and Responsible Learning (ARRL) .

    Paper submissions should be limited to a maximum of 8 pages, and follow the IEEE ICDM format. More detailed information is available in the IEEE ICDM 2023 Submission Guidelines.

    All accepted papers will be included in the ICDM'23 Workshop Proceedings (ICDMW 2023) published by the IEEE Computer Society Press. Therefore, papers must not have been accepted for publication elsewhere or be under review for another workshop, conferences or journals.

    All accepted papers, including workshops, must have at least one “FULL” registration. A full registration is either a “member” or “non-member” registration. Student registrations are not considered full registrations. All authors are required to register by 15th October 2023.

    For registration queries please contact:




Program Chairs

Contact information

Xingquan Zhu, Ph.D.
Dept. of Electrical Engineering and Computer Science
Florida Atlantic University
777 Glades Road, EE-503B
Tel: 561-297-3452
Webpage: Homepage

                  Yi He, Ph.D.
                  Assistant Professor
                  Department of Computer Science
                  Old Dominion University
                  3108 ECS Building, Norfolk, VA 23529
                  Tel: 757-683-7821
                  Webpage: Homepage