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

Be designed for the benefit, safety and privacy of the patient

Principle: GE Healthcare AI principles, Oct 1, 2018 (unconfirmed)

Published by GE Healthcare

Related Principles

· Control Risks

Continuous efforts should be made to improve the maturity, robustness, reliability, and controllability of AI systems, so as to ensure the security for the data, the safety and security for the AI system itself, and the safety for the external environment where the AI system deploys.

Published by Beijing Academy of Artificial Intelligence (BAAI); Peking University; Tsinghua University; Institute of Automation, Chinese Academy of Sciences; Institute of Computing Technology, Chinese Academy of Sciences; Artifical Intelligence Industry Innovation Strategy Alliance (AITISA); etc. in Beijing AI Principles, May 25, 2019

6. Principle of privacy

Developers should take it into consideration that AI systems will not infringe the privacy of users or third parties. [Comment] The privacy referred to in this principle includes spatial privacy (peace of personal life), information privacy (personal data), and secrecy of communications. Developers should consider international guidelines on privacy, such as “OECD Guidelines on the Protection of Privacy and Transborder Flows of Personal Data,” as well as the followings, with consideration of the possibility that AI systems might change their outputs or programs as a result of learning and other methods: ● To make efforts to evaluate the risks of privacy infringement and conduct privacy impact assessment in advance. ● To make efforts to take necessary measures, to the extent possible in light of the characteristics of the technologies to be adopted throughout the process of development of the AI systems (“privacy by design”), to avoid infringement of privacy at the time of the utilization.

Published by Ministry of Internal Affairs and Communications (MIC), the Government of Japan in AI R&D Principles, Jul 28, 2017

· Prepare Input Data:

1 Following the best practice of responsible data acquisition, handling, classification, and management must be a priority to ensure that results and outcomes align with the AI system’s set goals and objectives. Effective data quality soundness and procurement begin by ensuring the integrity of the data source and data accuracy in representing all observations to avoid the systematic disadvantaging of under represented or advantaging over represented groups. The quantity and quality of the data sets should be sufficient and accurate to serve the purpose of the system. The sample size of the data collected or procured has a significant impact on the accuracy and fairness of the outputs of a trained model. 2 Sensitive personal data attributes which are defined in the plan and design phase should not be included in the model data not to feed the existing bias on them. Also, the proxies of the sensitive features should be analyzed and not included in the input data. In some cases, this may not be possible due to the accuracy or objective of the AI system. In this case, the justification of the usage of the sensitive personal data attributes or their proxies should be provided. 3 Causality based feature selection should be ensured. Selected features should be verified with business owners and non technical teams. 4 Automated decision support technologies present major risks of bias and unwanted application at the deployment phase, so it is critical to set out mechanisms to prevent harmful and discriminatory results at this phase.

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

Plan and Design:

1 The planning and design of the AI system and its associated algorithm must be configured and modelled in a manner such that there is respect for the protection of the privacy of individuals, personal data is not misused and exploited, and the decision criteria of the automated technology is not based on personally identifying characteristics or information. 2 The use of personal information should be limited only to that which is necessary for the proper functioning of the system. The design of AI systems resulting in the profiling of individuals or communities may only occur if approved by Chief Compliance and Ethics Officer, Compliance Officer or in compliance with a code of ethics and conduct developed by a national regulatory authority for the specific sector or industry. 3 The security and protection blueprint of the AI system, including the data to be processed and the algorithm to be used, should be aligned to best practices to be able to withstand cyberattacks and data breach attempts. 4 Privacy and security legal frameworks and standards should be followed and customized for the particular use case or organization. 5 An important aspect of privacy and security is data architecture; consequently, data classification and profiling should be planned to define the levels of protection and usage of personal data. 6 Security mechanisms for de identification should be planned for the sensitive or personal data in the system. Furthermore, read write update actions should be authorized for the relevant groups.

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

· 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

Be designed for the benefit, safety and privacy of the patient

Principle: GE Healthcare AI principles, Oct 1, 2018 (unconfirmed)

Related Principles

· Control Risks

Continuous efforts should be made to improve the maturity, robustness, reliability, and controllability of AI systems, so as to ensure the security for the data, the safety and security for the AI system itself, and the safety for the external environment where the AI system deploys.

6. Principle of privacy

· Prepare Input Data:

Plan and Design:

· Build and Validate:

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