Periodic Reporting for period 1 - Giskard (Quality Assurance for AI)
Periodo di rendicontazione: 2023-09-01 al 2024-06-30
The rapid adoption of AI technologies has precipitated a corresponding rise in concerns regarding ethical biases, prediction errors, and cybersecurity risks. Current AI quality tools predominantly rely on manual testing methods, creating a gap that strains the capacity and resources of AI/ML engineers amidst escalating demands.
In response to these challenges, GISKARD is developing a comprehensive AI Quality Assurance and Compliance solution comprising both open-source and Software-as-a-Service (SaaS) platforms. These platforms are specifically designed to enhance the quality assurance processes for AI models across industries. GISKARD's initiative focuses on automating AI Quality Testing, Inspection, and Remediation, thereby empowering organizations to effectively manage and mitigate risks associated with AI deployments. This abstract outlines GISKARD's commitment to advancing AI quality assurance & Compliance practices through innovative technological solutions, aimed at addressing critical challenges in AI deployment and ensuring the reliability and ethical integrity of AI systems in practical applications, with a keen focus on how to make the EU AI Act applicable in the industry.
In response to these challenges, GISKARD is developing a comprehensive AI Quality Assurance and Compliance solution comprising both open-source and Software-as-a-Service (SaaS) platforms. These platforms are specifically designed to enhance the quality assurance processes for AI models across industries. GISKARD's initiative focuses on automating AI Quality Testing, Inspection, and Remediation, thereby empowering organizations to effectively manage and mitigate risks associated with AI deployments. This abstract outlines GISKARD's commitment to advancing AI quality assurance & Compliance practices through innovative technological solutions, aimed at addressing critical challenges in AI deployment and ensuring the reliability and ethical integrity of AI systems in practical applications, with a keen focus on how to make the EU AI Act applicable in the industry.
During the EIC project, Giskard has successfully completed its AI Inspect and the enhancement of automatic bias detection capabilities. Giskard is actively developing synthetic data augmentation modules aimed at addressing biases inherent in AI models. Beyond technical milestones, Giskard has also filed 2 patents and conducted extensive communication and dissemination activities to broaden its market presence.
Key accomplishments include:
1. Development of the AI Inspect automatic detection components, branded as model insight features
2. Implementation of the AI Test feature within the Giskard enterprise solution, featuring a comprehensive AI test catalog and the capability to execute tests across diverse AI environments with multi-ML worker support.
3. Introduction of a synthetic data generation solution in the Giskard open-source library, empowering AI practitioners to generate edge case data for rigorous AI model testing. This solution also offers actionable recommendations to enable data scientists in refining their models effectively.
Key accomplishments include:
1. Development of the AI Inspect automatic detection components, branded as model insight features
2. Implementation of the AI Test feature within the Giskard enterprise solution, featuring a comprehensive AI test catalog and the capability to execute tests across diverse AI environments with multi-ML worker support.
3. Introduction of a synthetic data generation solution in the Giskard open-source library, empowering AI practitioners to generate edge case data for rigorous AI model testing. This solution also offers actionable recommendations to enable data scientists in refining their models effectively.
The development of GISKARD's open-source and SaaS solution for AI quality assurance represents a significant advancement, addressing critical gaps in current manual testing. By automating AI Quality Testing, Inspection, and Remediation, Giskard aims to enhance reliability and mitigate risks such as ethical biases and security vulnerabilities. This innovation is developed by a team of experience Machine Learning Researchers, led by Dr. Jean-Marie John-Mathews, Ph.D. in AI Ethics, and former AI auditor for the European Agency for Fundamental Rights.
Potential impacts include streamlining AI deployment across industries, fostering innovation, and building stakeholder trust in AI. Key needs for further uptake and success include ongoing research and development to keep pace with technology, demonstrating effectiveness across diverse applications, accessing markets and finance for scalability, robust commercialization strategies, strong intellectual property rights protection, international expansion, and advocating for supportive regulatory frameworks for AI, including laws and standards. Addressing these needs will maximize Giskard's impact, accelerate market adoption, and establish it as a leader in trustworthy AI deployment worldwide.
Potential impacts include streamlining AI deployment across industries, fostering innovation, and building stakeholder trust in AI. Key needs for further uptake and success include ongoing research and development to keep pace with technology, demonstrating effectiveness across diverse applications, accessing markets and finance for scalability, robust commercialization strategies, strong intellectual property rights protection, international expansion, and advocating for supportive regulatory frameworks for AI, including laws and standards. Addressing these needs will maximize Giskard's impact, accelerate market adoption, and establish it as a leader in trustworthy AI deployment worldwide.