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

  • Present novel and original work. 

  • Follow the submission format as main conference (in the same template, max. 16 pages including references). 

  • Are ‘best-effort’ anonymized (follow double-blind instructions for the main conference). 

  • Authors of accepted full papers can opt for their camera-ready papers to be included in the ECML PKDD post-workshop proceedings of the CCIS series of Springer.

• Abstracts

  • Present already published work, including papers in other conferences or journals, demo's, datasets, or projects that are publicly available.

  • Consist of max. 2 pages, excluding references.

  • Are not expected to be anonymized.

  • Will not be published in the proceedings.

Both types of papers may be submitted here on CMT. The deadline is 15.06.2024.

Topics of interest: 

We invite contributions that deal with bias and fairness in any ML approach (including but not limited to supervised learning, unsupervised learning, ranking, generative models, etc.) and ML system (e.g., recommender systems, search engines, chatbots, 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 fairness auditing/assessment of ML systems, surrounding topics like:

• Auditing practices, tools, and metrics

• Bias in generative and multimodal models

• Best practices and legal frameworks around audits

• Case studies

• Privacy-aware fairness audits

• xAI for understanding/auditing biases

• Visual analytics for understanding/auditing biases

• Society’s perception of algorithmic fairness

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

• Fairness-aware learning in multimodal and multi-attribute data

• Fairness-aware data collection

• Fairness-aware data processing

• Fairness-aware algorithms

Important dates: 

Paper Submission Deadline: 15.06.2024
Paper Acceptance Notification: 15.07.2024
Workshop Date: 13.09.2024

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