At this workshop, we wish to stimulate the exchange of novel ideas and interdisciplinary perspectives. To do this, we will accept three different types of submissions:
(A) Full papers, presenting novel and original work (max. 16 pages incl. references, same submission format as main conference)
(B) Abstracts of unpublished work (max. 2 pages excl. references)
(C) Previously-published peer-reviewed papers (long and short papers)
Papers of type (A) and (B) must be written in English and formatted in LaTeX, following the Springer LNCS format. The program chairs reserve the right to reject any over-length papers without review. Papers that ‘cheat’ the page limit by, including but not limited to, using smaller than specified margins or font sizes will also be treated as over-length.
Paper of type (C), please see the detailed instructions below.
Please note that only submissions of type (A) will be included in the workshop proceedings.
All papers must be submitted through this CMT link
We invite submissions of previously published, peer-reviewed papers to BIAS 2026 @ ECML/PKDD. This track is intended for papers that have appeared at reputable venues and will undergo a lighter review process. Submissions of this type are non-archival.
Eligible submissions include full or short papers published at leading venues, including but not limited to NeurIPS, ICLR, ICML, AAAI, FAccT, AIES, CHI, RecSys, SIGIR, ECML/PKDD, and ECAI. Papers must have been accepted or published after January 2024.
Instructions:
Submit a short statement describing the relevance of the paper to the BIAS workshop, along with the paper’s DOI link. If a DOI is not yet available due to recent acceptance, authors should provide an arXiv link or a similar publicly accessible version.
Additionally, submit a PDF of the published or camera-ready version of the paper. The submission should remain in the original formatting of the publication venue; reformatting to the workshop template is not required.
We invite contributions that deal with bias and fairness in any ML/AI approach (including but not limited to supervised learning, unsupervised learning, ranking, generative models, etc.) and application (e.g., recommender systems, search engines, chatbots, content moderation, etc.) on any type of data (tables, text, images, videos, speech, multimodal, etc.) and learning setup (batch, non i.i.d., federated, etc.).
We invite submissions in the following areas but are open to submissions on other topics related to the area of AI bias and fairness.
Bias audits and evaluation practices, including topics like:
Auditing and compliance practices, tools, and metrics
Bias in generative and multimodal models
Best practices and legal frameworks around audits
Case studies
Privacy-aware fairness audits
xAI and visual analytics for understanding/auditing biases
Society’s perception of algorithmic fairness
Bias-aware system development and deployment:
Bias-aware learning in multimodal and multi-attribute data
Bias-aware data collection
Bias-aware data processing
Human in the loop approaches for fairness
Case studies on bias-aware systems and applications
Interplay with other learning objectives and trustworthy AI dimensions
Accuracy, fairness, privacy and robustness trade-offs
xAI for fairness and bias in xAI
Paper Submission Deadline: 05 June 2026
Paper Acceptance Notification: 03 July 2026
Workshop Date: 07 September 2026
All Deadlines are Anytime on Earth (AoE) at 23:59.