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Call for Workshop

2024 IEEE International Working Workshop on AI System Quality Assurance and Standards

Important Dates:

  • Presentation abstract: 6/15/2024
  • Workshop papers:7/15/2024
  • Posted Conference Proceeding Camera Ready: 7/30/2024
  • Keynote speaker registration: 7/5/2024
  • Invited speaker registration: 7/5/2024
  • Early registration for non-authors: 6/15/2024

 

Call for Workshop:

Fast advances of AI technologies and evolutionary machine learning bring a strong demand on AI standards and quality assurance control systems to ensure high-quality of deployed AI systems in trustworthy, safety, and security.

 

The Imperative for AI Standards

The integration of AI into critical sectors of the economy necessitates a framework that ensures these technologies are safe, reliable, and effective. Without standardized metrics for quality and security, the potential for inefficient or unsafe applications of AI increases.

 

The Importance of Global AI Standards

Establishing global standards for AI technologies is not merely a regulatory goal; it is a strategic imperative that affects the global economy and technological innovation. Standardized AI frameworks foster interoperability among systems and countries, facilitating smoother transitions and integrations across international borders. These standards also help in minimizing the risk of bias and ensuring fairness in AI applications, promoting trust and confidence among users worldwide.

 

Importance of AI Quality standards:

  • Well-defined AI system quality standards are critical to support continuous improvement of AI-powered system quality outcomes and efficient adherence to regulatory AI system intelligence requirements and compliance.
  • Effective and systematic quality assurance and evaluation standards and programs are necessary to assure system trustworthy, safety, and human-friendly for diverse AI solutions and intelligence systems.
  • Existing system quality standards does not consider and address AI system features and needs in quality assurance.
  • Diverse AI algorithms, machine learning models, and intelligent application systems lack well-defined quality assurance standards.

 

Objectives:

To address urgent needs for AI standard and intelligent system quality assurance, IEEE CISOSE congress, as an active research community in intelligence and service-oriented system engineering, recognizes both necessities and importance of establishing AI standards and quality assurance. This working workshop is setup to address this urgent and critical demand, and provides the first platform to support academic researchers, industry quality assurance groups, and practitioners to propose, discuss, and exchange AI intelligence and application system quality assurance issues, ideas, metrics, standards, and control systems.

 

Workshop Topics:

We seek original work, contributions, and presentations on machine learning models and intelligent systems quality assurance and standardization. Topics of interest include, but are not limited to:

  • Quality assurance challenges and needs for machine learning models and AI systems
  • Quality assurance processes for AI applications and intelligent systems
  • AI and intelligent system quality assessment methods and approaches
  • Big data quality validation and QA standards
  • Quality assurance metrics for machine learning models and AI technologies
  • Quality evaluation models and standards for big data-driven machine learning models
  • Quality assurance metrics and standards for NLP machine learning models and NLP-based smart chatbot systems
  • Quality assurance metrics and standards for computer vision models and application systems
  • Quality control evaluation and systems for AI techniques and intelligent systems
  • Quality validation and automation tools and systems
  • Big data Safety and security validation and assessment for AI and machine learning models
  • Quality assurance for smart machines (intelligent robots, smart autonomous vehicles, and smart UAV systems)
  • Industry and practical quality control experience report and lesson learned

All of the submitted work and presentation should not be published by any other journal/magazine/conference/workshop.

 

Workshop Chairs

  • Jerry Gao, San Jose State University, USA
  • Jie Xu, University of Leeds, UK
  • Hong Zhu, Oxford Brooks University, UK

 

Program Chairs:

  • Chuanqi Tao, Nanjing University of Aeronautics & Astronautics, USA
  • Oum-El-Kheir Aktouf, Université Grenoble Alpes, France

 

Organizing Committee Members:

  • Global Industry Alliance Chair: Jane Wu, BRI
  • TBCIS Liaison: Kuo-Ming Chao, Boumenoith University, UK
  • China Industry Connectivity Chair: Daniel Zhu
    Publicity Chair: RuPing Liu, Taiyuan University of Technologies, China
  • Proceeding Chair: Shenqiang Lu, Computer Network Information Center, Chinese Academy of Sciences(Xi’an)

 

Submission site:

 

Paper Submission:

All of papers and presentation abstracts should be prepared in IEEE Conference Proceeding Format (https://www.ieee.org/conferences/publishing/templates.html). All accepted papers, abstracts, and presentations will be presented in the IEEE Working Workshop, and its proceedings will be published by IEEE Computer Society and IEEE Explore digital library.

 

Registrations:

  • Distinguished Keynote Speaker – Free
  • Invited Grand/Season Speaker: $100
  • Invited Speaker – $400
  • Regular Speaker: $500
  • Non-presentation registration: $200 (early registration before 7/1) and $300 (late registration after 7/1)