The nexus between artificial intelligence (AI) and big data analytics is digitally transforming many industries by deploying intelligence systems in real-time, including healthcare, finance, robotics, and transportation. Designing and deploying algorithms that are reliable, robust, and secure is desirable for trustworthy systems with high-stakes decision making. For instance, AI-assisted robotic surgery, automated financial trading, autonomous driving and many more modern applications are vulnerable to concept drifts, dataset shifts, misspecifications, perturbations, and adversarial attacks beyond human or even machine comprehension level, thereby posing dangerous threats to various stakeholders at different levels. Moreover, building trustworthy AI systems requires lots of research efforts in addressing different mechanisms and approaches that could enhance user and public trust. To name a few, following topics are known to be topic of interests in Trustworthy AI, but not limited to: (i) bias and fairness, (ii) explainability, (iii) robust mitigation of adversarial attacks, (iv) improved privacy and security in model building, (v) being decent, and (vi) model attribution. All of these topics are important and need to be addressed.
This special session aims to draw together state-of-the-art advances in machine learning (ML) to address challenges for ensuring reliability in trustworthy systems. The challenges in different learning paradigms are including, but not limited to (1) robust learning, (2) adversarial learning, (3) stochastic, deterministic and non-deterministic learning. Nonetheless, all aspects of learning algorithms that can deal with reliable & robust issues are the focus of the special session. It will focus on robustness and performance guarantee, as well as, consistency, transparency and safety of AI which is vital to ensure reliability. The special session will attract analytics experts to build trustworthy AI systems by developing and assessing theoretical and empirical methods, practical applications, and new ideas and identify directions for future studies. Original contributions as well as comparative studies among different methods are welcome with unbiased literature review.
Topics of Interest
Topics of the special session include (with reliable/robustness/secure learning methods), including but not limited to:
The 28th International Conference on Neural Information Processing (ICONIP2021)
December 8 - 12, 2021, Virtually (old plan: hybrid mode in
The 28th International Conference on Neural Information Processing (ICONIP2021) aims to provide a leading international forum for researchers, scientists, and industry professionals who are working in neuroscience, neural networks, deep learning, and related fields to share their new ideas, progresses and achievement, through its regular sessions, special sessions, tutorials, and workshops.
ICONIP 2021 will be held virtually, during December 8-12, 2021.