Call for papers

Submission details: 

At this workshop, we wish to stimulate the exchange of novel ideas and interdisciplinary perspectives. To do this, we will accept two different types of submissions:

• Full papers of at most 16 pages (incl. references) that present novel and original work. These papers must be fully anonymized and authors can choose to have them published in Post-Workshop proceedings by Springer Communications in Computer and Information Science

• Abstracts of 2-4 pages (incl. references) that present already published work, including papers in other conferences, journals, demo's, datasets, or projects that are publicly available. Abstracts are non-archival and non-anonymous.

Papers 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. 

All papers must be submitted through this CMT link

Topics of interest: 

We invite contributions that deal with bias and fairness in any AI approach (including but not limited to supervised learning, unsupervised learning, ranking, large language models, etc.) and AI system (e.g., recommender systems, search engines, chatbots, AI agents, content moderation, etc.) on any type of data (tables, text, images, videos, speech, multimodal, ...) and learning setup (batch, non i.i.d., federated, …). We especially welcome interdisciplinary work, bridging computer science with fields like human-computer interaction, law, and social sciences. 

Contributions may concern the bias auditing/assessment of AI systems, surrounding topics like:

• Auditing practices, tools, and metrics

• Bias in general-purpose AI and multimodal models

• Bias in AI agents

• Best practices and legal frameworks around audits

• Case studies

• Privacy-aware bias audits

• xAI for understanding/auditing biases

• Visual analytics for understanding/auditing biases

• Society’s perception of algorithmic fairness

• Societal impact of biases

Other contributions may deal with the development and deployment of fairer algorithms:

• Human in the loop approaches for fairness

• Case studies on fairness-aware algorithms

• Debiasing general-purpose AI models

• Fairness-aware data collection

• Fairness-aware data processing

• Fairness-aware algorithms

Important dates: 

Paper Submission Deadline: 14.06.2025
Paper Acceptance Notification: 14.07.2025
Workshop Date: 15.09.25 (morning)

All Deadlines are Anytime on Earth (AoE) at 23:59.