3. Given the non deterministic nature of intelligent systems, a system of constant tests and validations must be established, including the inputs made to the system and its overall behavior. The architecture of AI systems must establish behavioral limits.

Principle: Declaration Of Ethics For The Development And Use Of Artificial Intelligence (unofficial translation), Feb 8, 2019 (unconfirmed)

Published by IA Latam

Related Principles

· 5. The Principle of Explicability: “Operate transparently”

Transparency is key to building and maintaining citizen’s trust in the developers of AI systems and AI systems themselves. Both technological and business model transparency matter from an ethical standpoint. Technological transparency implies that AI systems be auditable, comprehensible and intelligible by human beings at varying levels of comprehension and expertise. Business model transparency means that human beings are knowingly informed of the intention of developers and technology implementers of AI systems. Explicability is a precondition for achieving informed consent from individuals interacting with AI systems and in order to ensure that the principle of explicability and non maleficence are achieved the requirement of informed consent should be sought. Explicability also requires accountability measures be put in place. Individuals and groups may request evidence of the baseline parameters and instructions given as inputs for AI decision making (the discovery or prediction sought by an AI system or the factors involved in the discovery or prediction made) by the organisations and developers of an AI system, the technology implementers, or another party in the supply chain.

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

· 2. RESPONSIBILITY MUST BE FULLY ACKNOWLEDGED WHEN CREATING AND USING AI

2.1. Risk based approach. The degree of attention paid to ethical AI issues and the nature of the relevant actions of AI Actors should be proportional to the assessment of the level of risk posed by specific AI technologies and systems for the interests of individuals and society. Risk level assessment shall take into account both known and possible risks, whereby the probability level of threats, as well as their possible scale in the short and long term shall be considered. Making decisions in the field of AI use that significantly affect society and the state should be accompanied by a scientifically verified, interdisciplinary forecast of socio economic consequences and risks and examination of possible changes in the paradigm of value and cultural development of the society. Development and use of an AI systems risk assessment methodology are encouraged in pursuance of this Code. 2.2. Responsible attitude. AI Actors should responsibly treat: • issues related to the influence of AI systems on society and citizens at every stage of the AI systems’ life cycle, i.a. on privacy, ethical, safe and responsible use of personal data; • the nature, degree and extent of damage that may result from the use of AI technologies and systems; • the selection and use of hardware and software utilized in different life cycles of AI systems. At the same time, the responsibility of AI Actors should correspond with the nature, degree and extent of damage that may occur as a result of the use of AI technologies and systems. The role in the life cycle of the AI system, as well as the degree of possible and real influence of a particular AI Actor on causing damage and its extent, should also be taken into account. 2.3. Precautions. When the activities of AI Actors can lead to morally unacceptable consequences for individuals and society, which can be reasonably predicted by the relevant AI Actor, the latter, should take measures to prohibit or limit the occurrence of such consequences. AI Actors shall use the provisions of this Code, including the mechanisms specified in Section 2, to assess the moral unacceptability of such consequences and discuss possible preventive measures. 2.4. No harm. AI Actors should not allow the use of AI technologies for the purpose of causing harm to human life and or health, the property of citizens and legal entities and the environment. Any use, including the design, development, testing, integration or operation of an AI system capable of purposefully causing harm to the environment, human life and or health, the property of citizens and legal entities, is prohibited. 2.5. Identification of AI in communication with a human. AI Actors are encouraged to ensure that users are duly informed of their interactions with AI systems when it affects human rights and critical areas of people’s lives and to ensure that such interaction can be terminated at the request of the user. 2.6. Data security. AI Actors must comply with the national legislation in the field of personal data and secrets protected by law when using AI systems; ensure the security and protection of personal data processed by AI systems or by AI Actors in order to develop and improve the AI systems; develop and integrate innovative methods to counter unauthorized access to personal data by third parties and use high quality and representative datasets obtained without breaking the law from reliable sources. 2.7. Information security. AI Actors should ensure the maximum possible protection from unauthorized interference of third parties in the operation of AI systems; integrate adequate information security technologies, i.a. use internal mechanisms designed to protect the AI system from unauthorized interventions and inform users and developers about such interventions; as well as promote the informing of users about the rules of information security during the use of AI systems. 2.8. Voluntary certification and Code compliance. AI Actors may implement voluntary certification systems to assess the compliance of developed AI technologies with the standards established by the national legislation and this Code. AI Actors may create voluntary certification and labeling systems for AI systems to indicate that these systems have passed voluntary certification procedures and confirm quality standards. 2.9. Control of the recursive self improvement of AI systems. AI Actors are encouraged to cooperate in identifying and verifying information about ways and forms of design of so called universal ("general") AI systems and prevention of possible threats they carry. The issues concerning the use of "general" AI technologies should be under the control of the state.

Published by AI Alliance Russia in AI Ethics Code (revised version), Oct 21, 2022 (unconfirmed)

· We will make AI systems accountable

1. Accountability for the outcomes of an AI system lies not with the system itself but is apportioned between those who design, develop and deploy it 2. Developers should make efforts to mitigate the risks inherent in the systems they design 3. AI systems should have built in appeals procedures whereby users can challenge significant decisions 4. AI systems should be developed by diverse teams which include experts in the area in which the system will be deployed

Published by Smart Dubai in Dubai's AI Principles, Jan 08, 2019

Second principle: Responsibility

Human responsibility for AI enabled systems must be clearly established, ensuring accountability for their outcomes, with clearly defined means by which human control is exercised throughout their lifecycles. The increased speed, complexity and automation of AI enabled systems may complicate our understanding of pre existing concepts of human control, responsibility and accountability. This may occur through the sorting and filtering of information presented to decision makers, the automation of previously human led processes, or processes by which AI enabled systems learn and evolve after their initial deployment. Nevertheless, as unique moral agents, humans must always be responsible for the ethical use of AI in Defence. Human responsibility for the use of AI enabled systems in Defence must be underpinned by a clear and consistent articulation of the means by which human control is exercised, and the nature and limitations of that control. While the level of human control will vary according to the context and capabilities of each AI enabled system, the ability to exercise human judgement over their outcomes is essential. Irrespective of the use case, Responsibility for each element of an AI enabled system, and an articulation of risk ownership, must be clearly defined from development, through deployment – including redeployment in new contexts – to decommissioning. This includes cases where systems are complex amalgamations of AI and non AI components, from multiple different suppliers. In this way, certain aspects of responsibility may reach beyond the team deploying a particular system, to other functions within the MOD, or beyond, to the third parties which build or integrate AI enabled systems for Defence. Collectively, these articulations of human control, responsibility and risk ownership must enable clear accountability for the outcomes of any AI enabled system in Defence. There must be no deployment or use without clear lines of responsibility and accountability, which should not be accepted by the designated duty holder unless they are satisfied that they can exercise control commensurate with the various risks.

Published by The Ministry of Defence (MOD), United Kingdom in Ethical Principles for AI in Defence, Jun 15, 2022

Fifth principle: Reliability

AI enabled systems must be demonstrably reliable, robust and secure. The MOD’s AI enabled systems must be suitably reliable; they must fulfil their intended design and deployment criteria and perform as expected, within acceptable performance parameters. Those parameters must be regularly reviewed and tested for reliability to be assured on an ongoing basis, particularly as AI enabled systems learn and evolve over time, or are deployed in new contexts. Given Defence’s unique operational context and the challenges of the information environment, this principle also requires AI enabled systems to be secure, and a robust approach to cybersecurity, data protection and privacy. MOD personnel working with or alongside AI enabled systems can build trust in those systems by ensuring that they have a suitable level of understanding of the performance and parameters of those systems, as articulated in the principle of understanding.

Published by The Ministry of Defence (MOD), United Kingdom in Ethical Principles for AI in Defence, Jun 15, 2022