Safe and Ethical AI (SEA) Platform Network · Linking Artificial Intelligence Principles (LAIP)

· 1) Robustness:

Artificial intelligence should be safe and reliable. We are dedicated to accentuating technical robustness and security throughout the research process, providing a secure and reliable system to improve the ability to prevent attack and conduct self repair.

Principle: Chinese Young Scientists’ Declaration on the Governance and Innovation of Artificial Intelligence, Aug 29, 2019

Published by Youth Work Committee of Shanghai Computer Society

Related Principles

· Article 5: Secure safe and controllable.

Ensure that AI systems operate securely safely, reliably, and controllably throughout their lifecycle. Evaluate system security safety and potential risks, and continuously improve system maturity, robustness, and anti tampering capabilities. Ensure that the system can be supervised and promptly taken over by humans to avoid the negative effects of loss of system control.

Published by Artificial Intelligence Industry Alliance (AIIA), China in Joint Pledge on Artificial Intelligence Industry Self-Discipline (Draft for Comment), May 31, 2019

II. Technical robustness and safety

Trustworthy AI requires algorithms to be secure, reliable and robust enough to deal with errors or inconsistencies during all life cycle phases of the AI system, and to adequately cope with erroneous outcomes. AI systems need to be reliable, secure enough to be resilient against both overt attacks and more subtle attempts to manipulate data or algorithms themselves, and they must ensure a fall back plan in case of problems. Their decisions must be accurate, or at least correctly reflect their level of accuracy, and their outcomes should be reproducible. In addition, AI systems should integrate safety and security by design mechanisms to ensure that they are verifiably safe at every step, taking at heart the physical and mental safety of all concerned. This includes the minimisation and where possible the reversibility of unintended consequences or errors in the system’s operation. Processes to clarify and assess potential risks associated with the use of AI systems, across various application areas, should be put in place.

Published by European Commission in Key requirements for trustworthy AI, Apr 8, 2019

3. Technical reliability, Safety and security

Artificial intelligence solutions should be able to make accurate and effective decisions, while providing adequate security and defense against external attacks. Artificial intelligence solutions should be extensively tested, used with care and monitored.

Published by Megvii in Artificial Intelligence Application Criteria, Jul 8, 2019

· Build and Validate:

1 Privacy and security by design should be implemented while building the AI system. The security mechanisms should include the protection of various architectural dimensions of an AI model from malicious attacks. The structure and modules of the AI system should be protected from unauthorized modification or damage to any of its components. 2 The AI system should be secure to ensure and maintain the integrity of the information it processes. This ensures that the system remains continuously functional and accessible to authorized users. It is crucial that the system safeguards confidential and private information, even under hostile or adversarial conditions. Furthermore, appropriate measures should be in place to ensure that AI systems with automated decision making capabilities uphold the necessary data privacy and security standards. 3 The AI System should be tested to ensure that the combination of available data does not reveal the sensitive data or break the anonymity of the observation. Deploy and Monitor: 1 After the deployment of the AI system, when its outcomes are realized, there must be continuous monitoring to ensure that the AI system is privacy preserving, safe and secure. The privacy impact assessment and risk management assessment should be continuously revisited to ensure that societal and ethical considerations are regularly evaluated. 2 AI System Owners should be accountable for the design and implementation of AI systems in such a way as to ensure that personal information is protected throughout the life cycle of the AI system. The components of the AI system should be updated based on continuous monitoring and privacy impact assessment.

Published by SDAIA in AI Ethics Principles, Sept 14, 2022

· Plan and Design:

1 Designing and developing an AI system that can withstand the uncertainty, instability, and volatility that it might encounter is crucial. 2 Planning to set out a robust and reliable AI system that works with different sets of inputs and situations is essential to prevent unintended harm and mitigate risks of system failures when positioned against unknown and unforeseen events. 3 Establishing a set of standards and protocols for assessing the reliability of an AI system is necessary to secure the safety of the system’s algorithm and data output. It is essential to keep a sustainable technical outlay and outcomes generated from the system to maintain the public’s trust and confidence in the AI system. 4 The documentation standards are essential to track the evolution of the system, foresee possible risks and fix vulnerabilities. 5 All critical decision points in the system design should be subject to sign off by relevant stakeholders to minimize risks and make stakeholders accountable for the decisions.

Published by SDAIA in AI Ethics Principles, Sept 14, 2022