(d) Justice, equity, and solidarity
AI should contribute to global justice and equal access to the benefits and advantages that AI, robotics and ‘autonomous’ systems can bring. Discriminatory biases in data sets used to train and run AI systems should be prevented or detected, reported and neutralised at the earliest stage possible.
We need a concerted global effort towards equal access to ‘autonomous’ technologies and fair distribution of benefits and equal opportunities across and within societies. This includes the formulating of new models of fair distribution and benefit sharing apt to respond to the economic transformations caused by automation, digitalisation and AI, ensuring accessibility to core AI technologies, and facilitating training in STEM and digital disciplines, particularly with respect to disadvantaged regions and societal groups. Vigilance is required with respect to the downside of the detailed and massive data on individuals that accumulates and that will put pressure on the idea of solidarity, e.g. systems of mutual assistance such as in social insurance and healthcare. These processes may undermine social cohesion and give rise to radical individualism.
• Foster Innovation and Open Development
To better understand the impact of AI and explore the broad diversity of AI implementations, public policy should encourage investment in AI R&D. Governments should support the controlled testing of AI systems to help industry, academia, and other stakeholders improve the technology.
• Fuel AI innovation: Public policy should promote investment, make available funds for R&D, and address barriers to AI development and adoption.
• Address global societal challenges: AI powered flagship initiatives should be funded to find solutions to the world’s greatest challenges such as curing cancer, ensuring food security, controlling climate change, and achieving inclusive economic growth.
• Allow for experimentation: Governments should create the conditions necessary for the controlled testing and experimentation of AI in the real world, such as designating self driving test sites in cities.
• Prepare a workforce for AI: Governments should create incentives for students to pursue courses of study that will allow them to create the next generation of AI.
• Lead by example: Governments should lead the way on demonstrating the applications of AI in its interactions with citizens and invest sufficiently in infrastructure to support and deliver AI based services.
• Partnering for AI: Governments should partner with industry, academia, and other stakeholders for the promotion of AI and debate ways to maximize its benefits for the economy.
1. Principle of collaboration
Published by: Ministry of Internal Affairs and Communications (MIC), the Government of Japan in AI R&D Principles
Developers should pay attention to the interconnectivity and interoperability of AI systems.
Developers should give consideration to the interconnectivity and interoperability between the AI systems that they have developed and other AI systems, etc. with consideration of the diversity of AI systems so that: (a) the benefits of AI systems should increase through the sound progress of AI networking; and that (b) multiple developers’ efforts to control the risks should be coordinated well and operate effectively. For this, developers should pay attention to the followings:
• To make efforts to cooperate to share relevant information which is effective in ensuring interconnectivity and interoperability.
• To make efforts to develop AI systems conforming to international standards, if any.
• To make efforts to address the standardization of data formats and the openness of interfaces and protocols including application programming interface (API).
• To pay attention to risks of unintended events as a result of the interconnection or interoperations between AI systems that they have developed and other AI systems, etc.
• To make efforts to promote open and fair treatment of license agreements for and their conditions of intellectual property rights, such as standard essential patents, contributing to ensuring the interconnectivity and interoperability between AI systems and other AI systems, etc., while taking into consideration the balance between the protection and the utilization with respect to intellectual property related to the development of AI.
The interoperability and interconnectivity in this context expects that AI systems which developers have developed can be connected to information and communication networks, thereby can operate with other AI systems, etc. in mutually and appropriately harmonized manners.
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.
2.5. International co operation for trustworthy AI
a) Governments, including developing countries and with stakeholders, should actively cooperate to advance these principles and to progress on responsible stewardship of trustworthy AI.
b) Governments should work together in the OECD and other global and regional fora to foster the sharing of AI knowledge, as appropriate. They should encourage international, crosssectoral and open multi stakeholder initiatives to garner long term expertise on AI.
c) Governments should promote the development of multi stakeholder, consensus driven global technical standards for interoperable and trustworthy AI.
d) Governments should also encourage the development, and their own use, of internationally comparable metrics to measure AI research, development and deployment, and gather the evidence base to assess progress in the implementation of these principles.