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FLAIRS-36 ST XAI, Bias, and Trust 2023 : FLAIRS Special Track on Explainability, Bias, and Trust in Artificial Intelligence

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Link: https://sites.google.com/view/flairs-35spectrackxaibiastrust/home
 
When May 14, 2023 - May 17, 2023
Where Clearwater Beach, FL
Abstract Registration Due Feb 6, 2023
Submission Deadline Feb 13, 2023
Notification Due Mar 13, 2023
Final Version Due Apr 10, 2023
Categories    artificial intelligence   XAI   bias   trust
 

Call For Papers

Call for Papers: FLAIRS-36 Special Track on Explainability, Bias, and Trust

Abstract Due Date: February 6, 2023
Submission Due Date: February 13, 2023
Conference Dates: May 14-17, 2023
Conference Location: Clearwater Beach, Florida

Website: https://sites.google.com/view/flairs-35spectrackxaibiastrust/home
URL: https://www.flairs-36.info/call-for-papers

We are seeking submissions for the Explainability, Bias, and Trust special track at the 36th International FLAIRS Conference (https://www.flairs-36.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.

Extended versions of select papers accepted to this track will be invited for consideration in a special issue of the International Journal on AI Tools (IJAIT).

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 dtalbert@tntech.edu or William Eberle at weberle@tntech.edu.

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