EXPLAINABLE AND RELIABLE MACHINE LEARNING IN SIGNALS & DATA SCIENCE
14:30-16:30, September 19, 2023, Tuesday
Session Chair: Zheng-Hua Tan
Machine learning, especially deep learning, has garnered remarkable success across various domains, serving as the driving force behind the ongoing AI revolution. Despite the success, there is a growing need to ensure that machine learning models are explainable, reliable, and sustainable for signal and data science applications. This thematic session is part of a series of efforts aimed at promoting activities and nurturing cross-disciplinary collaboration in AI. It follows two noteworthy events: a) NSF-IEEE Workshop: Toward Explainable, Reliable, and Sustainable Machine Learning in Signal and Data Science, March 2023, College Park, MD and b) IEEE Journal of Selected Topics in Signal Processing (JSTSP) Special Series on AI in Signal & Data Science – Toward Explainable, Reliable, and Sustainable Machine Learning.
- Tülay Adali (University of Maryland Baltimore County, USA)
- Klaus-Robert Müller (TU Berlin, Germany)
- David Miller (Pennsylvania State University, USA)
- Sijia Liu (Michigan State University, USA)