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.

Submission Guidelines

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 2024 Submission Guidelines.

All accepted papers will be included in the ICDM'24 Workshop Proceedings (ICDMW 2024) 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: registration@computer.org

List of Topics

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

Adaptable Learning

• Online/Incremental Learning
• Transfer Learning and Domain Adaptation
• Lifelong/Continual/Meta Learning
• Learning from Heterogeneous and Multi-Modal Data
• Knowledge Discovery from Multiple Databases
• Learning with Rejection/Abstention
• Cross-Domain Data Mining
• Evolving Data Stream Mining
• Ensemble Learning in Dynamic Environments

Reliable Learning

• Robustness and Generalization in Data Mining
• Trustworthiness in Learning-enabled Systems
• Noise Handling and Outlier/Anomaly Detection
• Data Wrangling and Munging for Reliable Preprocessing
• Data Quality Assessment and Assurance
• Robustness in Graph and Network Mining
• Uncertainty Quantification and Confidence Estimation in Learning-enabled Systems
• Learning with Very Few Examples
• Open-World Learning (Learning in Unexpected/Unknown Environments)

Responsible Learning

• Explainable Learning Modules and Architectures
• Interpretability of Learning Results
• Algorithmic Fairness in Data Mining
• Discrimination-aware Data Mining
• Privacy-Preserving Data Mining
• Ethical Data Mining and Data Usage
• Socio-technical Aspects of Data Mining
• Bias Detection and Mitigation in Learning-enabled Systems
• AI for Environmental and Social Sustainability
• Data Mining for Energy Efficiency and Climate Action

Organizing Chairs

• Heitor Murilo Gomes, Assistant Professor, Victoria University of Wellington. Email: heitor.gomes@vuw.ac.nz
• Yi He, Victoria University of Wellington, College of William and Mary. Email: yihe@wm.edu
• Xingquan Zhu, Professor, Florida Atlantic University. Email: xzhu3@fau.edu