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

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.