Conferences
26 February 2024, Vancouver, BC, Canada
Artificial Intelligence with Biased or Scarce Data 2024
With the increasing appetite for data in data-driven methods, the issues of biased and scarce data have become a major bottleneck in developing generalizable and scalable artificial intelligence solutions, as well as effective uses of these solutions in real-world scenarios. To tackle these challenges, researchers from both academia and industry must collaborate to make progress in fundamental research and applied technologies. The organizing committee and keynote speakers of AIBSD 2024 consist of experts from both academia and industry with rich experience in designing and developing robust artificial intelligence algorithms and transferring them to real-world solutions. AIBSD 2024 provides a focused opportunity to discuss and disseminate research related to bias and scarcity topics in artificial intelligence.
Submission Instructions
We welcome full paper submissions (up to 7 pages, excluding references or supplementary materials). Please submit papers at the following CMT website:
AIBSD 2024 CMT submission website.
The paper submissions must be in pdf format and use the AAAI 2024 official templates. All submissions must be anonymous and conform to the AAAI 2024 standards for double-blind reviews. The accepted papers will be posted on the workshop website and will not appear in the AAAI 2024 proceedings. At least one author of each accepted submission must present the paper at the workshop in person.
We invite the submission of original and high-quality research papers in the topics related to biased or scarce data. Accepted work will be presented as either an oral or spotlight presentation.
Scope
The topics for AIBSD 2024 include, but are not limited to, the following:
- Algorithms and theories for explainable and interpretable AI models;
- Application-specific designs for explainable AI, e.g., healthcare, autonomous driving, etc.;
- Algorithms and theories for learning AI models under bias and scarcity;
- Performance characterization of AI algorithms and systems under bias and scarcity;
- Algorithms for secure and privacy-aware machine learning for AI;
- Algorithms and theories for trustworthy AI models;
- The role of adjacent fields of study (e.g., computational social science) in mitigating issues of bias and trust in AI;
- Continuous refinement of AI models using active/online learning;
- Meta-learning models from various existing task-specific AI models;
- Limitation of or methods incorporating large language model under bias and/or data scarcity settings;
- Brave new ideas to learn AI models under bias and scarcity.
