Submission Instructions

We welcome full paper submissions (up to 7 pages, excluding references or supplementary materials). Please submit at the following CMT website:
AIBSD 2026 CMT submission website. 

The paper submissions must be in pdf format and use the AAAI 2026 official templates. All submissions must be anonymous and conform to the AAAI 2026 standards for double-blind review. The accepted papers will be posted on the workshop website and will not appear in the AAAI 2026 proceedings. At least one author of each accepted submission must present the paper at the workshop in person.  

Submission deadline: October 22, 2025 11:59 PM EST  
Notification to authors: November 5, 2025
Camera ready deadline:  November 12, 2025 11:59 PM EST

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, spotlight, or poster presentation. By submitting a paper to the AIBSD 2026 workshop for review, the authors must agree that they are willing and able to serve as the reviewers of the AIBSD 2026 workshop submissions if needed (decided by the AIBSD 2026 workshop organizing team); otherwise, their submissions are subject to desk rejection. 

We're seeking dedicated Reviewers! Please self-nominate via the reviewer self-nomination form. Thanks for your support!

Topics

The topics for AIBSD 2026 include, but are not limited to:

• 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.

Oral Papers

• DINO-Mix: Distilling Foundational Knowledge with Cross-Domain CutMix for Semi-supervised Class-imbalanced Medical Image Segmentation.
  Liu, Xinyu; Sun, Guolei.
  


• Generating on Generated: An Approach Towards Self-Evolving Diffusion Models.
  Zhang, Xulu; Wei, Xiaoyong; Wu, Jinlin; Wu, Jiaxin; Li, Qing.
  


• BAT: Backbone Augmented Training for Adaptations.
  Park, Jae Wan; Kim, Junhyeok; Jun, Youngjun; Ko, Hyunah; Hwang, Seong Jae.
  


• STELLAR: Scene Text Editor for Low-Resource Languages and Real-World Data.
  Seo, Yongdeuk; Min, Hyun-seok; Choi, Sungchul.
  [supplement]


• Surface-Based Visibility-Guided Uncertainty for Continuous 3D Active Neural Reconstruction.
  Kim, Hyunseo; Yang, Hyeonseo; Kim, Taekyung; Kim, YoonSung; Lee, Minsu; Kim, Jin-Hwa; Zhang, Byoung-Tak.
  [supplement]

Poster Papers

• On Modality Incomplete Infrared-Visible Object Detection: An Architecture Compatibility Perspective.
  Yang, Shuo; Xing, Yinghui; Zhang, Shizhou; Niu, Zhilong.
  


• Training data membership inference via Gaussian process meta-modeling: a post-hoc analysis approach.
  Huang, Yongchao; Zhang, Pengfei; Mumtaz, Shahzad.
  


• Imputation of Unknown Missingness in Sparse Electronic Health Records.
  Han, Jun; Nassar, Josue; Batra, Sanjit; Cordova Palomera, Aldo; Nori, Vijay; Tillman, Robert.
  


• Hierarchical Interests Modeling on Multiple Time Scales for Sequential Recommendation.
  Kang, Wooseung; Kim, Minje; Lee, Minjae; Kim, Ungsik; Bae, Jiho; Lee, Suwon; Kim, Gun-Woo; Choi, Sang-Min.
  


• Towards Globally Interpretable Few-Shot Tabular Learning: Distilling Foundation Models.
  Soegeng, Hans; Guerand, Tristan; Peyrin, Thomas.
  


• Exploring the Effects of Alignment on Numerical Bias in Large Language Models.
  Sato, Ayako; Kim, Hwichan; Chen, Zhousi; Mita, Masato; Komachi, Mamoru.
  


• Impacts of Individual Fairness on Group Fairness from the Perspective of Generalized Entropy.
  Jin, Youngmi; Gim, Jio; Lee, Tae-Jin; Suh, Young-Joo.
  


• Localization-Confidence-Aware Pseudo-Label Selection for YOLO-based Semi-Supervised Object Detection.
  Kawano, Ken; Yamamura, Keiichiro; Yoshida, Akihiro; Ishikura, Hiroki; Hirai, Shinjiro; Fujisawa, Yoshihiko; Fujisawa, Katsuki.
  


• Cross-Lingual Transfer Learning for Enhanced Synthetic Scanpath Prediction in Reading.
  Stebakov, Ivan.
  


• Lost in Transition: Addressing the Transitioning Heads Challenge in Long-Tailed Class-Incremental Learning.
  Vigneswaran, Rahul; Kuchibhotla, Hari Chandana; N Balasubramanian, Vineeth.