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

1. Principle of proper utilization

Users should make efforts to utilize AI systems or AI services in a proper scope and manner, under the proper assignment of roles between humans and AI systems, or among users. [Main points to discuss] A) Utilization in the proper scope and manner On the basis of the provision of information and explanation from developers, etc. and with consideration of social contexts and circumstances, users may be expected to use AI in the proper scope and manner. In addition, users may be expected to recognize benefits and risks, understand proper uses, acquire necessary knowledge and skills and so on before using AI, according to the characteristics, usage situations, etc. of AI. Furthermore, users may be expected to check regularly whether they use AI in an appropriate scope and manner. B) Proper balance of benefits and risks of AI AI service providers and business users may be expected to take into consideration proper balance between benefits and risks of AI, including the consideration of the active use of AI for productivity and work efficiency improvements, after appropriately assessing risks of AI. C) Updates of AI software and inspections repairs, etc. of AI Through the process of utilization, users may be expected to make efforts to update AI software and perform inspections, repairs, etc. of AI in order to improve the function of AI and to mitigate risks. D) Human Intervention Regarding the judgment made by AI, in cases where it is necessary and possible (e.g., medical care using AI), humans may be expected to make decisions as to whether to use the judgments of AI, how to use it etc. In those cases, what can be considered as criteria for the necessity of human intervention? In the utilization of AI that operates through actuators, etc., in the case where it is planned to shift to human operation under certain conditions, what kind of matters are expected to be paid attention to? [Points of view as criteria (example)] • The nature of the rights and interests of indirect users, et al., and their intents, affected by the judgments of AI. • The degree of reliability of the judgment of AI (compared with reliability of human judgment). • Allowable time necessary for human judgment • Ability expected to be possessed by users E) Role assignments among users With consideration of the volume of capabilities and knowledge on AI that each user is expected to have and ease of implementing necessary measures, users may be expected to play such roles as seems to be appropriate and also to bear the responsibility. F) Cooperation among stakeholders Users and data providers may be expected to cooperate with stakeholders and to work on preventive or remedial measures (including information sharing, stopping and restoration of AI, elucidation of causes, measures to prevent recurrence, etc.) in accordance with the nature, conditions, etc. of damages caused by accidents, security breaches, privacy infringement, etc. that may occur in the future or have occurred through the use of AI. What is expected reasonable from a users point of view to ensure the above effectiveness?

Published by Ministry of Internal Affairs and Communications (MIC), the Government of Japan in Draft AI Utilization Principles, Jul 17, 2018

· Transparency and explainability

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. 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. 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. 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. 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 Draft Text of 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