The world prepares for pervasive AI

Artificial Intelligence has the potential to profoundly change human society, and to become a new engine of economic development. Around the world, governments are implementing AI strategies to ensure readiness for a new round of industrial transformation, with the birth of new technologies, new products, new industries, new formats, and new business models.  These developments however, will trigger significant changes in economic structure, transformation of employment structures, violations of personal privacy, and other challenges.

As AI strategies and AI policy are designed, great importance must be given to the potential safety risks and challenges. The challenges and opportunities are immense. To that effect, a working relationship between government, private industry, startup ecosystems and academia should be necessary to establishing a set of principles for AI and making it human-centric.

Areas of Concern

Some of the main concerns around AI, have to do with the need for an emphasis on establishing standards of fairness and equity in AI, and a need to mainstream AI safety research. Lack of skills for successful implementations, and the expected number of jobs that will be threatened by AI are also top priorities as governments define new policies.

Research consistently finds that the jobs that will be threatened by AI are highly concentrated among lower paid, lower skilled and less educated workers. This means that automation will continue to put downward pressure on demand for this group, putting downward pressure on wages and upward pressure on inequality. However, given appropriate attention and the right policy and institutional responses, advanced automation can be compatible with productivity, high levels of employment, and more broadly shared prosperity. 

Countries with Formalized AI Strategies

China has laid out a development plan to become the world leader in A.I. by 2030, aiming to surpass its rivals technologically and build a domestic industry worth almost $150 billion. But many other countries are developing strategies to address this opportunity.  In total, these countries represent about 75% of global GDP: $60T: Australia, Canada, China, Denmark, EU Commission, Finland, France, Germany, India, Italy, Japan, Kenya, Malaysia, Mexico, New Zealand, Nordic-Baltic Region, Poland, Russia, Singapore, South Korea, Sweden, Taiwan, Tunisia, UAE, United Kingdom, United States.

AI Policy

Governments are approaching AI through different strategies, but most initiatives will tend to fall in the following categories according to Tim Dutton's research found at AI Policy 101

1.    Basic and Applied Research. Providing funding (grants) and creating research institutes

2.    Talent Attraction, Development, and Retention. Development of new education programs

3.    Future of Work and Skills: retraining programs, and transition to a lifelong learning model

4.    Industrialization of AI Technologies: Investing and developing AI ecosystems

5.    AI in the Government: reforming public administration and making policy more effective 

6.    Data and Digital Infrastructure: Open datasets and platforms for secure exchange of data

7.    Ethics: Developing ethical codes and standards for the use and development of AI. 

8.    Regulations: autonomous cars, autonomous weapons, facial recognition, privacy, etc

9.    Inclusion: Finding ways to bolster inclusion and address societal problems

10.  Foreign Policy: exploring mechanisms for global governance of AI

As governments define their policies and strategies, skills have the potential of becoming the main currency for economic development as the world transitions into the 4thindustrial revolution:

• Employees will need to be lifelong learners
• Businesses will need to reskill and upskill their workers and provide workers with improved guidance to navigate job transitions
• Governments should provide and/or support retraining programs
• Universities and academic institutions should expand their micro-credential programs
• Diversity and Inclusion in stem fields should be prioritized to reduce algorithmic bias