f) Reasonable thrift

Reasonable thrift: the prioritized implementation and adaptation of existing measures aimed at the execution of government policies in the scientific, technical, and other fields, and;
Principle: Basic Principles of the Development and Use of Artificial Intelligence Technologies, Oct 10, 2019

Published by Office of the President of the Russian Federation, Decree of the President of the Russian Federation on the Development of Artificial Intelligence in the Russian Federation

Related Principles

(Conclusion)
Taking into consideration the principles above, the 40th International Conference of Data Protection and Privacy Commissioners calls for common governance principles on artificial intelligence to be established, fostering concerted international efforts in this field, in order to ensure that its development and use take place in accordance with ethics and human values, and respect human dignity. These common governance principles must be able to tackle the challenges raised by the rapid evolutions of artificial intelligence technologies, on the basis of a multi stakeholder approach in order to address all cross sectoral issues at stake. They must take place at an international level since the development of artificial intelligence is a trans border phenomenon and may affect all humanity. The Conference should be involved in this international effort, working with and supporting general and sectoral authorities in other fields such as competition, market and consumer regulation.

Published by 40th International Conference of Data Protection and Privacy Commissioners (ICDPPC) in Declaration On Ethics And Data Protection In Artifical Intelligence, Oct 23, 2018

Chapter 6. Organization and Implementation
　　23. This set of norms is issued by the National Governance Committee of New Generation Artificial Intelligence, and it is responsible for explaining and guiding its implementation. 　　24. With actual requirements and needs, management departments at all levels, enterprises, universities, research institutes, associations and other related organizations may formulate more specific ethical norms and related measures based on this set of norms. 　　25. This set of norms shall be carried out starting from the date of its publication, and shall be revised in due course according to the needs of economic and social development and the development state of AI.

Published by National Governance Committee for the New Generation Artificial Intelligence, China in Ethical Norms for the New Generation Artificial Intelligence, Sep 25, 2021

Plan and Design:
1 This step is crucial to design or procure an AI System in an accountable and responsible manner. The ethical responsibility and liability for the outcomes of the AI system should be attributable to stakeholders who are responsible for certain actions in the AI System Lifecycle. It is essential to set a robust governance structure that defines the authorization and responsibility areas of the internal and external stakeholders without leaving any areas of uncertainty to achieve this principle. The design approach of the AI system should respect human rights, and fundamental freedoms as well as the national laws and cultural values of the kingdom. 2 Organizations can put in place additional instruments such as impact assessments, risk mitigation frameworks, audit and due diligence mechanisms, redress, and disaster recovery plans. 3 It is essential to build and design a human controlled AI system where decisions on the processes and functionality of the technology are monitored and executed, and are susceptible to intervention from authorized users. Human governance and oversight establish the necessary control and levels of autonomy through set mechanisms.

Published by SDAIA in AI Ethics Principles, Sept 14, 2022

3. Scientific Integrity and Information Quality
The government’s regulatory and non regulatory approaches to AI applications should leverage scientific and technical information and processes. Agencies should hold information, whether produced by the government or acquired by the government from third parties, that is likely to have a clear and substantial influence on important public policy or private sector decisions (including those made by consumers) to a high standard of quality, transparency, and compliance. Consistent with the principles of scientific integrity in the rulemaking and guidance processes, agencies should develop regulatory approaches to AI in a manner that both informs policy decisions and fosters public trust in AI. Best practices include transparently articulating the strengths, weaknesses, intended optimizations or outcomes, bias mitigation, and appropriate uses of the AI application’s results. Agencies should also be mindful that, for AI applications to produce predictable, reliable, and optimized outcomes, data used to train the AI system must be of sufficient quality for the intended use.

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

3. Scientific Integrity and Information Quality
The government’s regulatory and non regulatory approaches to AI applications should leverage scientific and technical information and processes. Agencies should hold information, whether produced by the government or acquired by the government from third parties, that is likely to have a clear and substantial influence on important public policy or private sector decisions (including those made by consumers) to a high standard of quality, transparency, and compliance. Consistent with the principles of scientific integrity in the rulemaking and guidance processes, agencies should develop regulatory approaches to AI in a manner that both informs policy decisions and fosters public trust in AI. Best practices include transparently articulating the strengths, weaknesses, intended optimizations or outcomes, bias mitigation, and appropriate uses of the AI application’s results. Agencies should also be mindful that, for AI applications to produce predictable, reliable, and optimized outcomes, data used to train the AI system must be of sufficient quality for the intended use.

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

Contact

Institute of Automation, Chinese Academy of Sciences
95 Zhongguancun East Road,
Haidian District, Beijing,
China 100190
Principal Investigator - Yi Zeng
contact@long-term-ai.cn

Contributors: Cunqing Huangfu, Enmeng Lu, Zizhe Ruan et al.

Copyright © 2018-
Brain-inspired Cognitive Intelligence Lab, Institute of Automation, Chinese Academy of Sciences
International Research Center for AI Ethics and Governance, Institute of Automation, Chinese Academy of Sciences
All Rights Reserved