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ECAF 2026 : Call for Papers: ECAF'26 - Fifth European Conference on Algorithmic Fairness

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Link: https://2026.ewaf.org/
 
When Sep 2, 2026 - Sep 4, 2026
Where Ghent, Belgium
Submission Deadline Apr 30, 2026
Notification Due Jun 16, 2026
Categories    interdisciplinary   fairness in ai   artificial intelligence
 

Call For Papers

Call for Papers: ECAF'26 - Fifth European Conference on Algorithmic Fairness
We are excited to invite submissions (full papers and extended abstracts) for the fifth European Conference on Algorithmic Fairness (ECAF’26) (https://2026.ewaf.org/), formerly the European Workshop on Algorithmic Fairness (EWAF), which will be held from September 2 - 4 2026 in Ghent, Belgium.


Important Dates
Submission deadline: Thursday, April 30, 2026 (AoE)
Acceptance notifications: Tuesday, June 16, 2023
Workshop dates: September 2 - 4, 2026


More information
Call for Papers: https://2026.ewaf.org/callForPapers
LinkedIn: https://www.linkedin.com/company/eaaf/
X: https://x.com/EWAFWorkshop
Bluesky: https://bsky.app/profile/ewafworkshop.bsky.social


About the Workshop
ECAF aims to foster dialogue between researchers working on algorithmic fairness in the context of Europe’s legal and societal framework, especially in light of the European Union’s attempts to promote ethical AI and the turn to AI for the common good.
ECAF welcomes submissions from multiple disciplines, including but not limited to computer science, law, philosophy, and social science, as well as interdisciplinary and transdisciplinary work. One of the primary goals of ECAF is to build a community and we encourage all participants to actively partake, e.g. by submitting an extended abstract of ongoing or recently published work. If you are planning to join us in Ghent, we strongly encourage you to make a submission!

Topics
We welcome submissions from the following areas:
• Computer science/data science/statistics: fairness metrics, methods for qualitative evaluations, bias mitigation techniques, auditing frameworks, fairness-aware data collection
• Philosophy/humanities: values embedded in distributive and procedural fairness, foundations of ethical AI, critical studies on AI
• Social Sciences: AI impact assessments, historical perspectives on discrimination, impact of algorithms on marginalized groups, perceptions of (un)fairness, AI and labor, digital governance, management and fairness, AI for public value creation
• Policy and Law: non-discrimination law, data protection law and data governance, impact assessments, accountability measures, sensitive application areas of AI (e.g., the judiciary, government, law enforcement, public services, global regulatory developments)

We also explicitly welcome submissions of interdisciplinary and transdisciplinary work, sourcing from multiple disciplinary areas and/or highlighting joint insight-building with relevant non-academic stakeholders.

A non-exhaustive list of themes includes:
• Industry experiences in developing and implementing fairness interventions, developing standards and practical approaches to introducing fairness in digital innovation governance.
• Empirical and theoretical perspectives from social sciences on fairness and discrimination in Europe (e.g., analysis of labor markets, the concepts of class, race, disability, and discrimination against minorities in different social contexts, intersectional inequality).
• Case studies based on concrete European instances of algorithmic design and regulation that machine learning scholars or practitioners have encountered in their work (e.g., datasets or audits of automated decision-making systems that are used in Europe).
• An analysis of the implications of the European legislative framework for the debate on fairness in machine learning and AI more broadly (e.g., specificities connected to anti-discrimination and data collection legislation and the emerging regulatory frameworks for platforms and AI).
• Principled arguments for certain fairness concepts and measures in specific contexts.
• Implementing fairness in deployed systems, selecting fairness definitions and designing auditing processes.
• Explorations of the relationship and trade-offs between fairness and transparency in practice.
• Fairness and transparency of black-box models.
• Generative AI and fairness, esp. relating to the job market and the data supply chain.

We also welcome submissions without a focus on European specificity or tackling other themes related to algorithmic fairness.


Submission information
Authors can choose between submitting a full paper or an extended abstract. For more details, please see the call for papers on our website: https://2026.ewaf.org/callForPapers


Organization
Toon Calders, Antwerp University (Belgium)
Dennis Nguyen, Utrecht University (The Netherlands)
Tijl De Bie, Ghent University (Belgium)
MaryBeth Defrance, Ghent University (Belgium)

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