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FLAIRS-37 ST XAI, Fairness, and Trust 2024 : FLAIRS-37 Special Track on Explainable, Fair, and Trustworthy AI


When May 18, 2024 - May 21, 2024
Where Miramar Beach, FL
Abstract Registration Due Jan 22, 2024
Submission Deadline Jan 29, 2024
Notification Due Mar 11, 2024
Final Version Due Apr 8, 2024
Categories    artificial intelligence   XAI   bias   trustworthy ai

Call For Papers

Call for Papers: FLAIRS-37 Special Track on Explainable, Fair, and Trustworthy AI

Abstract Due Date: January 22, 2024
Submission Due Date: January 29, 2024
Conference Dates: May 18-21, 2024
Conference Location: Miramar Beach, Florida


We are seeking submissions for the Explainable, Fair, and Trustworthy AI special track at the 37th International FLAIRS Conference ( This special track focuses on Explainable, Fair, and Trustworthy Artificial Intelligence systems. The goal of this track is to provide a venue for researchers to disseminate important and novel work in these areas and to bring such research to the diverse AI community that FLAIRS attracts. As AI continues to flourish and impact an increasingly broad array of industries and everyday activities, it is important to develop systems that users trust. The blackbox nature of many AI systems as well as well-publicized cases of bias in machine learning models undermine users’ trust in AI and lead to ethical and legal concerns. Explainable AI and bias detection and mitigation are active and growing areas of research designed to address these challenges.

Papers and contributions are encouraged for any work relating to AI and explainability, bias, or trust. Topics of interest may include (but are in no way limited to):

1. Detection and mitigation of bias in AI
2. Explainability of AI systems
3. Increasing trust in AI systems
4. Evaluating explainability and trust in AI
5. Support technologies useful for research in explainability, bias, and/or trust
6. Data sets of value in research in explainability, bias, and/or trust
7. Case studies of deployed systems involving explainability, bias, and/or trust

Questions regarding the track should be addressed to: Doug Talbert at

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