BS ISO/IEC 24029-2:2023 Artificial intelligence (AI). Assessment of the robustness of neural networks Methodology for the use of formal methods

Standard number: BS ISO/IEC 24029-2:2023

Pages: 32

Released: 2024-08-16

ISBN: 978 0 539 19425 8

Status: Standard

Overview

In the rapidly evolving field of Artificial Intelligence (AI), ensuring the robustness and reliability of neural networks is paramount. The BS ISO/IEC 24029-2:2023 standard provides a comprehensive methodology for assessing the robustness of neural networks using formal methods. This standard is an essential resource for AI developers, researchers, and organizations aiming to enhance the dependability of their AI systems.

Key Features

• Comprehensive Methodology: This standard outlines a detailed and systematic approach to evaluating the robustness of neural networks, ensuring that AI systems can withstand various challenges and uncertainties.
• Formal Methods: Utilizes formal methods to provide a rigorous framework for the assessment, offering a high level of assurance in the robustness of neural networks.
• Industry Relevance: Developed by leading experts in the field, this standard is aligned with the latest advancements and best practices in AI and neural network research.
• Global Standard: As an ISO/IEC standard, it is recognized and adopted internationally, facilitating global collaboration and consistency in AI robustness assessment.

Benefits

Adopting the BS ISO/IEC 24029-2:2023 standard offers numerous benefits, including:

• Enhanced Reliability: By following the methodologies outlined in this standard, organizations can significantly improve the reliability and robustness of their AI systems.
• Risk Mitigation: Identifying and addressing potential vulnerabilities in neural networks helps mitigate risks associated with AI deployment.
• Regulatory Compliance: Adhering to this internationally recognized standard can aid in meeting regulatory requirements and industry standards.
• Competitive Advantage: Demonstrating a commitment to AI robustness can enhance an organization's reputation and provide a competitive edge in the market.

Target Audience

This standard is designed for a wide range of stakeholders in the AI ecosystem, including:

• AI Developers: Engineers and developers working on neural network models can leverage this standard to ensure their systems are robust and reliable.
• Researchers: Academics and researchers can use the methodologies outlined in this standard to advance their studies in AI robustness.
• Organizations: Companies and institutions deploying AI solutions can adopt this standard to enhance the dependability of their AI systems.
• Regulators: Regulatory bodies can reference this standard to establish guidelines and requirements for AI robustness.

Structure and Content

The BS ISO/IEC 24029-2:2023 standard is structured to provide clear and actionable guidance. It includes:

• Introduction: An overview of the importance of robustness in neural networks and the role of formal methods in assessing it.
• Methodology: Detailed steps and procedures for conducting robustness assessments, including the use of formal methods.
• Case Studies: Real-world examples and case studies illustrating the application of the methodologies.
• References: A comprehensive list of references and further reading materials for those seeking to deepen their understanding.

Why Choose BS ISO/IEC 24029-2:2023?

In an era where AI is becoming increasingly integral to various industries, ensuring the robustness of neural networks is crucial. The BS ISO/IEC 24029-2:2023 standard stands out as a vital resource for achieving this goal. By adopting this standard, you are not only enhancing the reliability of your AI systems but also contributing to the broader goal of safe and dependable AI deployment.

Conclusion

The BS ISO/IEC 24029-2:2023 standard is an indispensable tool for anyone involved in the development, deployment, or regulation of AI systems. Its comprehensive methodology and use of formal methods provide a robust framework for assessing and ensuring the reliability of neural networks. Embrace this standard to stay at the forefront of AI innovation and reliability.

BS ISO/IEC 24029-2:2023

This standard BS ISO/IEC 24029-2:2023 Artificial intelligence (AI). Assessment of the robustness of neural networks is classified in these ICS categories:

• 35.020 Information technology (IT) in general