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XAI_Bias_Trust@FLAIRS 2022 : FLAIRS Special Track on Explainability, Bias, and Trust in Artificial Intelligence

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Link: https://sites.google.com/view/flairs-35spectrackxaibiastrust
 
When May 15, 2022 - May 18, 2022
Where Jensen Beach, FL, USA
Abstract Registration Due Jan 17, 2022
Submission Deadline Jan 24, 2022
Notification Due Feb 22, 2022
Final Version Due Mar 8, 2022
Categories    artificial intelligence   XAI   bias   trust
 

Call For Papers

Call for Papers: FLAIRS-35 Special Track on Explainability, Bias, and Trust

Abstract Due Date: January 17, 2022
Submission Due Date: January 24, 2022
Conference Dates: May 15-18, 2022
Conference Location: Jensen Beach, Florida

Website: https://sites.google.com/view/flairs-35spectrackxaibiastrust
URL: https://www.flairs-35.info/call-for-papers

We are seeking submissions for the Explainability, Bias, and Trust special track at the 35th International FLAIRS Conference (https://www.flairs-35.info/home). This special track focuses on Explainability, Bias, and Trust in 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):
- Detection and mitigation of bias in AI
- Explainability of AI systems
- Increasing trust in AI systems
- Evaluating explainability and trust in AI
- Support technologies useful for research in explainability, bias, and/or trust
- Data sets of value in research in explainability, bias, and/or trust
- Case studies of deployed systems involving explainability, bias, and/or trust

Questions regarding the track should be addressed to: Doug Talbert at dtalbert@tntech.edu, Michael Youngblood at Michael.Youngblood@parc.com, or William Eberle at weberle@tntech.edu.

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