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

3. An A.I. system cannot retain or disclose confidential information without explicit approval from the source of that information.

Principle: Three Rules for Artificial Intelligence Systems, Sep 1, 2017

Published by Oren Etzioni, CEO of Allen Institute for Artificial Intelligence

Related Principles

· (3) Privacy

In society premised on AI, it is possible to estimate each person’s political position, economic situation, hobbies preferences, etc. with high accuracy from data on the data subject’s personal behavior. This means, when utilizing AI, that more careful treatment of personal data is necessary than simply utilizing personal information. To ensure that people are not suffered disadvantages from unexpected sharing or utilization of personal data through the internet for instance, each stakeholder must handle personal data based on the following principles. Companies or government should not infringe individual person’s freedom, dignity and equality in utilization of personal data with AI technologies. AI that uses personal data should have a mechanism that ensures accuracy and legitimacy and enable the person herself himself to be substantially involved in the management of her his privacy data. As a result, when using the AI, people can provide personal data without concerns and effectively benefit from the data they provide. Personal data must be properly protected according to its importance and sensitivity. Personal data varies from those unjust use of which would be likely to greatly affect rights and benefits of individuals (Typically thought and creed, medical history, criminal record, etc.) to those that are semi public in social life. Taking this into consideration, we have to pay enough attention to the balance between the use and protection of personal data based on the common understanding of society and the cultural background.

Published by Cabinet Office, Government of Japan in Social Principles of Human-centric AI, Dec 27, 2018

· 7. Respect for Privacy

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

3 PROTECTION OF PRIVACY AND INTIMACY PRINCIPLE

Privacy and intimacy must be protected from AIS intrusion and data acquisition and archiving systems (DAAS). 1) Personal spaces in which people are not subjected to surveillance or digital evaluation must be protected from the intrusion of AIS and data acquisition and archiving systems (DAAS). 2) The intimacy of thoughts and emotions must be strictly protected from AIS and DAAS uses capable of causing harm, especially uses that impose moral judgments on people or their lifestyle choices. 3) People must always have the right to digital disconnection in their private lives, and AIS should explicitly offer the option to disconnect at regular intervals, without encouraging people to stay connected. 4) People must have extensive control over information regarding their preferences. AIS must not create individual preference proﬁles to inﬂuence the behavior of the individuals without their free and informed consent. 5) DAAS must guarantee data conﬁdentiality and personal proﬁle anonymity. 6) Every person must be able to exercise extensive control over their personal data, especially when it comes to its collection, use, and dissemination. Access to AIS and digital services by individuals must not be made conditional on their abandoning control or ownership of their personal data. 7) Individuals should be free to donate their personal data to research organizations in order to contribute to the advancement of knowledge. 8) The integrity of one’s personal identity must be guaranteed. AIS must not be used to imitate or alter a person’s appearance, voice, or other individual characteristics in order to damage one’s reputation or manipulate other people.

Published by University of Montreal in The Montreal Declaration for a Responsible Development of Artificial Intelligence, Dec 4, 2018

5. Data Provenance

A description of the way in which the training data was collected should be maintained by the builders of the algorithms, accompanied by an exploration of the potential biases induced by the human or algorithmic data gathering process. Public scrutiny of the data provides maximum opportunity for corrections. However, concerns over privacy, protecting trade secrets, or revelation of analytics that might allow malicious actors to game the system can justify restricting access to qualified and authorized individuals.

Published by ACM US Public Policy Council (USACM) in Principles for Algorithmic Transparency and Accountability, Jan 12, 2017

1 Protect autonomy

Adoption of AI can lead to situations in which decision making could be or is in fact transferred to machines. The principle of autonomy requires that any extension of machine autonomy not undermine human autonomy. In the context of health care, this means that humans should remain in full control of health care systems and medical decisions. AI systems should be designed demonstrably and systematically to conform to the principles and human rights with which they cohere; more specifically, they should be designed to assist humans, whether they be medical providers or patients, in making informed decisions. Human oversight may depend on the risks associated with an AI system but should always be meaningful and should thus include effective, transparent monitoring of human values and moral considerations. In practice, this could include deciding whether to use an AI system for a particular health care decision, to vary the level of human discretion and decision making and to develop AI technologies that can rank decisions when appropriate (as opposed to a single decision). These practicescan ensure a clinician can override decisions made by AI systems and that machine autonomy can be restricted and made “intrinsically reversible”. Respect for autonomy also entails the related duties to protect privacy and confidentiality and to ensure informed, valid consent by adopting appropriate legal frameworks for data protection. These should be fully supported and enforced by governments and respected by companies and their system designers, programmers, database creators and others. AI technologies should not be used for experimentation or manipulation of humans in a health care system without valid informed consent. The use of machine learning algorithms in diagnosis, prognosis and treatment plans should be incorporated into the process for informed and valid consent. Essential services should not be circumscribed or denied if an individual withholds consent and that additional incentives or inducements should not be offered by either a government or private parties to individuals who do provide consent. Data protection laws are one means of safeguarding individual rights and place obligations on data controllers and data processors. Such laws are necessary to protect privacy and the confidentiality of patient data and to establish patients’ control over their data. Construed broadly, data protection laws should also make it easy for people to access their own health data and to move or share those data as they like. Because machine learning requires large amounts of data – big data – these laws are increasingly important.

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