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

· 9. Safety

Safety is about ensuring that the system will indeed do what it is supposed to do, without harming users (human physical integrity), resources or the environment. It includes minimizing unintended consequences and errors in the operation of the system. Processes to clarify and assess potential risks associated with the use of AI products and services should be put in place. Moreover, formal mechanisms are needed to measure and guide the adaptability of AI systems.

Principle: Draft Ethics Guidelines for Trustworthy AI, Dec 18, 2018

Published by The European Commission’s High-Level Expert Group on Artificial Intelligence

Related Principles

Transparency and explainability

There should be transparency and responsible disclosure to ensure people know when they are being significantly impacted by an AI system, and can find out when an AI system is engaging with them. This principle aims to ensure responsible disclosure when an AI system is significantly impacting on a person’s life. The definition of the threshold for ‘significant impact’ will depend on the context, impact and application of the AI system in question. Achieving transparency in AI systems through responsible disclosure is important to each stakeholder group for the following reasons for users, what the system is doing and why for creators, including those undertaking the validation and certification of AI, the systems’ processes and input data for those deploying and operating the system, to understand processes and input data for an accident investigator, if accidents occur for regulators in the context of investigations for those in the legal process, to inform evidence and decision‐making for the public, to build confidence in the technology Responsible disclosures should be provided in a timely manner, and provide reasonable justifications for AI systems outcomes. This includes information that helps people understand outcomes, like key factors used in decision making. This principle also aims to ensure people have the ability to find out when an AI system is engaging with them (regardless of the level of impact), and are able to obtain a reasonable disclosure regarding the AI system.

Published by Department of Industry, Innovation and Science, Australian Government in AI Ethics Principles, Nov 7, 2019

· 2. NEED FOR CONSCIOUS RESPONSIBILITY WHEN CREATING AND USING AI

2.1. Risk based approach. The level of attention to ethical issues in AI and the nature of the relevant actions of AI Actors should be proportional to the assessment of the level of risk posed by specific technologies and AISs and the interests of individuals and society. Risk level assessment must take into account both the known and possible risks; in this case, the level of probability of threats should be taken into account as well as their possible scale in the short and long term. In the field of AI development, making decisions that are significant to society and the state should be accompanied by scientifically verified and interdisciplinary forecasting of socio economic consequences and risks, as well as by the examination of possible changes in the value and cultural paradigm of the development of society, while taking into account national priorities. In pursuance of this Code, the development and use of an AIS risk assessment methodology is recommended. 2.2. Responsible attitude. AI Actors should have a responsible approach to the aspects of AIS that influence society and citizens at every stage of the AIS life cycle. These include privacy; the ethical, safe and responsible use of personal data; the nature, degree and amount of damage that may follow as a result of the use of the technology and AIS; and the selection and use of companion hardware and software. In this case, the responsibility of the AI Actors must correspond to the nature, degree and amount of damage that may occur as a result of the use of technologies and AIS, while taking into account the role of the AI Actor in the life cycle of AIS, as well as the degree of possible and real impact of a particular AI Actor on causing damage, as well as its size. 2.3. Precautions. When the activities of AI Actors can lead to morally unacceptable consequences for individuals and society, the occurrence of which the corresponding AI Actor can reasonably assume, measures should be taken to prevent or limit the occurrence of such consequences. To assess the moral acceptability of consequences and the possible measures to prevent them, Actors can use the provisions of this Code, including the mechanisms specified in Section 2. 2.4. No harm. AI Actors should not allow use of AI technologies for the purpose of causing harm to human life, the environment and or the health or property of citizens and legal entities. Any application of an AIS capable of purposefully causing harm to the environment, human life or health or the property of citizens and legal entities during any stage, including design, development, testing, implementation or operation, is unacceptable. 2.5. Identification of AI in communication with a human. AI Actors are encouraged to ensure that users are informed of their interactions with the AIS when it affects their rights and critical areas of their lives and to ensure that such interactions can be terminated at the request of the user. 2.6. Data security AI Actors must comply with the legislation of the Russian Federation in the field of personal data and secrets protected by law when using an AIS. Furthermore, they must ensure the protection and protection of personal data processed by an AIS or AI Actors in order to develop and improve the AIS by developing and implementing innovative methods of controlling unauthorized access by third parties to personal data and using high quality and representative datasets from reliable sources and obtained without breaking the law. 2.7. Information security. AI Actors should provide the maximum possible protection against unauthorized interference in the work of the AI by third parties by introducing adequate information security technologies, including the use of internal mechanisms for protecting the AIS from unauthorized interventions and informing users and developers about such interventions. They must also inform users about the rules regarding information security when using the AIS. 2.8. Voluntary certification and Code compliance. AI Actors can implement voluntary certification for the compliance of the developed AI technologies with the standards established by the legislation of the Russian Federation and this Code. AI Actors can create voluntary certification and AIS labeling systems that indicate that these systems have passed voluntary certification procedures and confirm quality standards. 2.9. Control of the recursive self improvement of AISs. AI Actors are encouraged to collaborate in the identification and verification of methods and forms of creating universal ("strong") AIS and the prevention of the possible threats that AIS carry. The use of "strong" AI technologies should be under the control of the state.

Published by AI Alliance Russia in Artificial Intelligence Code of Ethics, Oct 26, 2021

· Transparency and explainability

37. The transparency and explainability of AI systems are often essential preconditions to ensure the respect, protection and promotion of human rights, fundamental freedoms and ethical principles. Transparency is necessary for relevant national and international liability regimes to work effectively. A lack of transparency could also undermine the possibility of effectively challenging decisions based on outcomes produced by AI systems and may thereby infringe the right to a fair trial and effective remedy, and limits the areas in which these systems can be legally used. 38. While efforts need to be made to increase transparency and explainability of AI systems, including those with extra territorial impact, throughout their life cycle to support democratic governance, the level of transparency and explainability should always be appropriate to the context and impact, as there may be a need to balance between transparency and explainability and other principles such as privacy, safety and security. People should be fully informed when a decision is informed by or is made on the basis of AI algorithms, including when it affects their safety or human rights, and in those circumstances should have the opportunity to request explanatory information from the relevant AI actor or public sector institutions. In addition, individuals should be able to access the reasons for a decision affecting their rights and freedoms, and have the option of making submissions to a designated staff member of the private sector company or public sector institution able to review and correct the decision. AI actors should inform users when a product or service is provided directly or with the assistance of AI systems in a proper and timely manner. 39. From a socio technical lens, greater transparency contributes to more peaceful, just, democratic and inclusive societies. It allows for public scrutiny that can decrease corruption and discrimination, and can also help detect and prevent negative impacts on human rights. Transparency aims at providing appropriate information to the respective addressees to enable their understanding and foster trust. Specific to the AI system, transparency can enable people to understand how each stage of an AI system is put in place, appropriate to the context and sensitivity of the AI system. It may also include insight into factors that affect a specific prediction or decision, and whether or not appropriate assurances (such as safety or fairness measures) are in place. In cases of serious threats of adverse human rights impacts, transparency may also require the sharing of code or datasets. 40. Explainability refers to making intelligible and providing insight into the outcome of AI systems. The explainability of AI systems also refers to the understandability of the input, output and the functioning of each algorithmic building block and how it contributes to the outcome of the systems. Thus, explainability is closely related to transparency, as outcomes and ub processes leading to outcomes should aim to be understandable and traceable, appropriate to the context. AI actors should commit to ensuring that the algorithms developed are explainable. In the case of AI applications that impact the end user in a way that is not temporary, easily reversible or otherwise low risk, it should be ensured that the meaningful explanation is provided with any decision that resulted in the action taken in order for the outcome to be considered transparent. 41. Transparency and explainability relate closely to adequate responsibility and accountability measures, as well as to the trustworthiness of AI systems.

Published by The United Nations Educational, Scientific and Cultural Organization (UNESCO) in The Recommendation on the Ethics of Artificial Intelligence, Nov 24, 2021

4 Foster responsibility and accountability

Humans require clear, transparent specification of the tasks that systems can perform and the conditions under which they can achieve the desired level of performance; this helps to ensure that health care providers can use an AI technology responsibly. Although AI technologies perform specific tasks, it is the responsibility of human stakeholders to ensure that they can perform those tasks and that they are used under appropriate conditions. Responsibility can be assured by application of “human warranty”, which implies evaluation by patients and clinicians in the development and deployment of AI technologies. In human warranty, regulatory principles are applied upstream and downstream of the algorithm by establishing points of human supervision. The critical points of supervision are identified by discussions among professionals, patients and designers. The goal is to ensure that the algorithm remains on a machine learning development path that is medically effective, can be interrogated and is ethically responsible; it involves active partnership with patients and the public, such as meaningful public consultation and debate (101). Ultimately, such work should be validated by regulatory agencies or other supervisory authorities. When something does go wrong in application of an AI technology, there should be accountability. Appropriate mechanisms should be adopted to ensure questioning by and redress for individuals and groups adversely affected by algorithmically informed decisions. This should include access to prompt, effective remedies and redress from governments and companies that deploy AI technologies for health care. Redress should include compensation, rehabilitation, restitution, sanctions where necessary and a guarantee of non repetition. The use of AI technologies in medicine requires attribution of responsibility within complex systems in which responsibility is distributed among numerous agents. When medical decisions by AI technologies harm individuals, responsibility and accountability processes should clearly identify the relative roles of manufacturers and clinical users in the harm. This is an evolving challenge and remains unsettled in the laws of most countries. Institutions have not only legal liability but also a duty to assume responsibility for decisions made by the algorithms they use, even if it is not feasible to explain in detail how the algorithms produce their results. To avoid diffusion of responsibility, in which “everybody’s problem becomes nobody’s responsibility”, a faultless responsibility model (“collective responsibility”), in which all the agents involved in the development and deployment of an AI technology are held responsible, can encourage all actors to act with integrity and minimize harm. In such a model, the actual intentions of each agent (or actor) or their ability to control an outcome are not considered.

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

6 Promote artificial intelligence that is responsive and sustainable

Responsiveness requires that designers, developers and users continuously, systematically and transparently examine an AI technology to determine whether it is responding adequately, appropriately and according to communicated expectations and requirements in the context in which it is used. Thus, identification of a health need requires that institutions and governments respond to that need and its context with appropriate technologies with the aim of achieving the public interest in health protection and promotion. When an AI technology is ineffective or engenders dissatisfaction, the duty to be responsive requires an institutional process to resolve the problem, which may include terminating use of the technology. Responsiveness also requires that AI technologies be consistent with wider efforts to promote health systems and environmental and workplace sustainability. AI technologies should be introduced only if they can be fully integrated and sustained in the health care system. Too often, especially in under resourced health systems, new technologies are not used or are not repaired or updated, thereby wasting scare resources that could have been invested in proven interventions. Furthermore, AI systems should be designed to minimize their ecological footprints and increase energy efficiency, so that use of AI is consistent with society’s efforts to reduce the impact of human beings on the earth’s environment, ecosystems and climate. Sustainability also requires governments and companies to address anticipated disruptions to the workplace, including training of health care workers to adapt to use of AI and potential job losses due to the use of automated systems for routine health care functions and administrative tasks.