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

Privacy and data protection must be guaranteed at all stages of the life cycle of the AI system. This includes all data provided by the user, but also all information generated about the user over the course of his or her interactions with the AI system (e.g. outputs that the AI system generated for specific users, how users responded to particular recommendations, etc.). Digital records of human behaviour can reveal highly sensitive data, not only in terms of preferences, but also regarding sexual orientation, age, gender, religious and political views. The person in control of such information could use this to his her advantage. Organisations must be mindful of how data is used and might impact users, and ensure full compliance with the GDPR as well as other applicable regulation dealing with privacy and data protection.

Published by The European Commission’s High-Level Expert Group on Artificial Intelligence in Draft Ethics Guidelines for Trustworthy AI, Dec 18, 2018

Published by IA Latam in Declaration Of Ethics For The Development And Use Of Artificial Intelligence (unofficial translation), Feb 8, 2019 (unconfirmed)

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

AI should be intelligible or understandable to developers, users and regulators. Two broad approaches to ensuring intelligibility are improving the transparency and explainability of AI technology. Transparency requires that sufficient information (described below) be published or documented before the design and deployment of an AI technology. Such information should facilitate meaningful public consultation and debate on how the AI technology is designed and how it should be used. Such information should continue to be published and documented regularly and in a timely manner after an AI technology is approved for use. Transparency will improve system quality and protect patient and public health safety. For instance, system evaluators require transparency in order to identify errors, and government regulators rely on transparency to conduct proper, effective oversight. It must be possible to audit an AI technology, including if something goes wrong. Transparency should include accurate information about the assumptions and limitations of the technology, operating protocols, the properties of the data (including methods of data collection, processing and labelling) and development of the algorithmic model. AI technologies should be explainable to the extent possible and according to the capacity of those to whom the explanation is directed. Data protection laws already create specific obligations of explainability for automated decision making. Those who might request or require an explanation should be well informed, and the educational information must be tailored to each population, including, for example, marginalized populations. Many AI technologies are complex, and the complexity might frustrate both the explainer and the person receiving the explanation. There is a possible trade off between full explainability of an algorithm (at the cost of accuracy) and improved accuracy (at the cost of explainability). All algorithms should be tested rigorously in the settings in which the technology will be used in order to ensure that it meets standards of safety and efficacy. The examination and validation should include the assumptions, operational protocols, data properties and output decisions of the AI technology. Tests and evaluations should be regular, transparent and of sufficient breadth to cover differences in the performance of the algorithm according to race, ethnicity, gender, age and other relevant human characteristics. There should be robust, independent oversight of such tests and evaluation to ensure that they are conducted safely and effectively. Health care institutions, health systems and public health agencies should regularly publish information about how decisions have been made for adoption of an AI technology and how the technology will be evaluated periodically, its uses, its known limitations and the role of decision making, which can facilitate external auditing and oversight.

Published by World Health Organization (WHO) in Key ethical principles for use of artificial intelligence for health, Jun 28, 2021