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

5. Benefits and Costs

When developing regulatory and non regulatory approaches, agencies will often consider the application and deployment of AI into already regulated industries. Presumably, such significant investments would not occur unless they offered significant economic potential. As in all technological transitions of this nature, the introduction of AI may also create unique challenges. For example, while the broader legal environment already applies to AI applications, the application of existing law to questions of responsibility and liability for decisions made by AI could be unclear in some instances, leading to the need for agencies, consistent with their authorities, to evaluate the benefits, costs, and distributional effects associated with any identified or expected method for accountability. Executive Order 12866 calls on agencies to “select those approaches that maximize net benefits (including potential economic, environmental, public health and safety, and other advantages; distributive impacts; and equity).” Agencies should, when consistent with law, carefully consider the full societal costs, benefits, and distributional effects before considering regulations related to the development and deployment of AI applications. Such consideration will include the potential benefits and costs of employing AI, when compared to the systems AI has been designed to complement or replace, whether implementing AI will change the type of errors created by the system, as well as comparison to the degree of risk tolerated in other existing ones. Agencies should also consider critical dependencies when evaluating AI costs and benefits, as technological factors (such as data quality) and changes in human processes associated with AI implementation may alter the nature and magnitude of the risks and benefits. In cases where a comparison to a current system or process is not available, evaluation of risks and costs of not implementing the system should be evaluated as well.

Principle: Principles for the Stewardship of AI Applications, Nov 17, 2020

Published by The White House Office of Science and Technology Policy (OSTP), United States

Related Principles

4. Principle of safety

Developers should take it into consideration that AI systems will not harm the life, body, or property of users or third parties through actuators or other devices. [Comment] AI systems which are supposed to be subject to this principle are such ones that might harm the life, body, or property of users or third parties through actuators or other devices. It is encouraged that developers refer to relevant international standards and pay attention to the followings, with particular consideration of the possibility that outputs or programs might change as a result of learning or other methods of AI systems: ● To make efforts to conduct verification and validation in advance in order to assess and mitigate the risks related to the safety of the AI systems. ● To make efforts to implement measures, throughout the development stage of AI systems to the extent possible in light of the characteristics of the technologies to be adopted, to contribute to the intrinsic safety (reduction of essential risk factors such as kinetic energy of actuators) and the functional safety (mitigation of risks by operation of additional control devices such as automatic braking) when AI systems work with actuators or other devices. And ● To make efforts to explain the designers’ intent of AI systems and the reasons for it to stakeholders such as users, when developing AI systems to be used for making judgments regarding the safety of life, body, or property of users and third parties (for example, such judgments that prioritizes life, body, property to be protected at the time of an accident of a robot equipped with AI).

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

· 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)

· 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

5. Benefits and Costs

When developing regulatory and non regulatory approaches, agencies will often consider the application and deployment of AI into already regulated industries. Presumably, such significant investments would not occur unless they offered significant economic potential. As in all technological transitions of this nature, the introduction of AI may also create unique challenges. For example, while the broader legal environment already applies to AI applications, the application of existing law to questions of responsibility and liability for decisions made by AI could be unclear in some instances, leading to the need for agencies, consistent with their authorities, to evaluate the benefits, costs, and distributional effects associated with any identified or expected method for accountability. Executive Order 12866 calls on agencies to “select those approaches that maximize net benefits (including potential economic, environmental, public health and safety, and other advantages; distributive impacts; and equity).” Agencies should, when consistent with law, carefully consider the full societal costs, benefits, and distributional effects before considering regulations related to the development and deployment of AI applications. Such consideration will include the potential benefits and costs of employing AI, when compared to the systems AI has been designed to complement or replace, whether implementing AI will change the type of errors created by the system, as well as comparison to the degree of risk tolerated in other existing ones. Agencies should also consider critical dependencies when evaluating AI costs and benefits, as technological factors (such as data quality) and changes in human processes associated with AI implementation may alter the nature and magnitude of the risks and benefits. In cases where a comparison to a current system or process is not available, evaluation of risks and costs of not implementing the system should be evaluated as well.

Published by The White House Office of Science and Technology Policy (OSTP), United States in Principles for the Stewardship of AI Applications, Nov 17, 2020

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.

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