Special Track on Artificial Intelligence Testing


This special track focuses on developing frameworks, methods, tools, and empirical studies for testing data-centric artificial intelligence (AI) systems, particularly on how to use state-of-the-art technology in assessment, assurance, and improvement of big data for building high-quality AI systems.


A list of particular relevant areas includes, but is not limited to:

  • Effective frameworks and strategies for testing AI systems
  • Data quality assessment, assurance, and improvement for AI applications
  • Experimental study regarding the impact of data quality to the performance of deep learning
  • Data augmentation for the enhancement of data-centric AI
  • Evaluating large language models in different AI applications
  • Responsibility, fairness, ethics, bias, trustworthiness, transparency, accountability, safety, and privacy in AI applications

SCI Special Issues

Authors of top quality papers will be invited to submit their extended versions to the following special issues:


Authors are invited to submit original unpublished research papers as well as industrial practice papers. Simultaneous submissions to other conferences are not permitted. Detailed instructions for electronic paper submission, panel proposals, and review process can be found at QRS submission.

Each submission can have a maximum of ten pages. It should include a title, the name and affiliation of each author, a 300-word abstract, and up to 6 keywords. Shorter version papers (up to six pages) are also allowed.

All papers must conform to the QRS conference proceedings format (PDF | Word DOCX | Latex) and Submission Guideline set in advance by QRS 2023. At least one of the authors of each accepted paper is required to pay the full registration fee and present the paper at the workshop. Submissions must be in PDF format and uploaded to the conference submission site. Arrangements are being made to publish extended version of top-quality papers in selected SCI journals.


Program Chairs

Junhua Ding's avatar
Junhua Ding

University of North Texas

Haihua Chen's avatar
Haihua Chen

University of North Texas