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

· Adaptation and Moderation

Adaptive revisions of AI principles, policies, and regulations should be actively considered to adjust them to the development of AI. Governance measures of AI should match its development status, not only to avoid hindering its proper utilization, but also to ensure that it is beneficial to society and nature.

Principle: Beijing AI Principles, May 25, 2019

Published by Beijing Academy of Artificial Intelligence (BAAI); Peking University; Tsinghua University; Institute of Automation, Chinese Academy of Sciences; Institute of Computing Technology, Chinese Academy of Sciences; Artifical Intelligence Industry Innovation Strategy Alliance (AITISA); etc.

Related Principles

· (1) Human centric

Utilization of AI should not infringe upon fundamental human rights that are guaranteed by the Constitution and international norms. AI should be developed and utilized and implemented in society to expand the abilities of people and to pursue the diverse concepts of happiness of diverse people. In the AI utilized society, it is desirable that we implement appropriate mechanisms of literacy education and promotion of proper uses, so as not to over depend on AI or not to ill manipulate human decisions by exploiting AI. AI can expand human abilities and creativity not only by replacing part of human task but also by assisting human as an advanced instrument. When using AI, people must judge and decide for themselves how to use AI. Appropriate stakeholders involved in the development, provision, and utilization of AI should be responsible for the result of AI utilization, depending on the nature of the issue. In order to avoid creating digital divide and allow all people to reap the benefit of AI regardless of their digital expertise, each stakeholder should take into consideration to user friendliness of the system in the process of AI deployment.

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

8. Agile Governance

The governance of AI should respect the underlying principles of AI development. In promoting the innovative and healthy development of AI, high vigilance should be maintained in order to detect and resolve possible problems in a timely manner. The governance of AI should be adaptive and inclusive, constantly upgrading the intelligence level of the technologies, optimizing management mechanisms, and engaging with muti stakeholders to improve the governance institutions. The governance principles should be promoted throughout the entire lifecycle of AI products and services. Continuous research and foresight for the potential risks of higher level of AI in the future are required to ensure that AI will always be beneficial for human society.

Published by National Governance Committee for the New Generation Artificial Intelligence, China in Governance Principles for the New Generation Artificial Intelligence--Developing Responsible Artificial Intelligence, Jun 17, 2019

· Proportionality and Do No Harm

25. It should be recognized that AI technologies do not necessarily, per se, ensure human and environmental and ecosystem flourishing. Furthermore, none of the processes related to the AI system life cycle shall exceed what is necessary to achieve legitimate aims or objectives and should be appropriate to the context. In the event of possible occurrence of any harm to human beings, human rights and fundamental freedoms, communities and society at large or the environment and ecosystems, the implementation of procedures for risk assessment and the adoption of measures in order to preclude the occurrence of such harm should be ensured. 26. The choice to use AI systems and which AI method to use should be justified in the following ways: (a) the AI method chosen should be appropriate and proportional to achieve a given legitimate aim; (b) the AI method chosen should not infringe upon the foundational values captured in this document, in particular, its use must not violate or abuse human rights; and (c) the AI method should be appropriate to the context and should be based on rigorous scientific foundations. In scenarios where decisions are understood to have an impact that is irreversible or difficult to reverse or may involve life and death decisions, final human determination should apply. In particular, AI systems should not be used for social scoring or mass surveillance purposes.

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