Comparing AI Strategy Documents: Lessons from Japan and the UK
Competing for AI Supremacy: Lessons from Japan and UK's Strategy Playbooks
As artificial intelligence rapidly transforms societies and economies, governments worldwide are releasing national AI strategy documents to prepare for the impacts and opportunities. I recently analyzed and compared two such documents - Japan's AI Strategy originally published in June 2019 and the UK's National AI Strategy published in September 2021. While both strategies aim to establish leadership in AI, the documents provide an interesting contrast in tone, scope and priorities. Links to both documents are below.
National AI strategies offer valuable insights into a country's vision, capabilities and challenges. Comparing strategies can highlight best practices and lessons learned that policymakers and industry leaders can apply to their AI programs. Here I'll share my key takeaways from analyzing Japan and the UK's AI plans.
Research Excellence vs. Economic Pragmatism
The UK's strategy focuses heavily on maintaining the country's strength in AI research. It aims to grow the number and diversity of "AI discoveries" made in the UK, calling for expanded research programs and global talent schemes. The document highlights notable homegrown AI companies like DeepMind and stresses the need for continued R&D investment.
In contrast, Japan's strategy is firmly rooted in economic objectives. It centeres on transforming industries, revitalizing the economy and boosting international competitiveness through AI adoption. Beyond supporting AI R&D centers, it outlines sector-specific goals like automating food production, modernizing healthcare and digitizing government administration.
This dichotomy seems to reflect the countries' respective strengths. The UK has an esteemed AI research tradition traced back to Alan Turing. Japan is better known for applying technologies like robotics and IoT in manufacturing and infrastructure. Both play to their competitive advantage, but the UK's "research first" approach could miss economic opportunities if discoveries aren't commercialized.
Addressing Near-Term vs. Long-Term Risks
Japan's strategy has an entire pillar dedicated to "dealing with imminent crises" like pandemics and natural disasters. It stresses the need for "AI for national resilience", detailing how technologies can maximize disaster preparedness and response. The UK document lacks this pragmatic focus on urgent, near-term risks.
At the same time, the UK strategy dives deep into long-term AI safety concerns including the speculative risks of artificial general intelligence. Though important, prolonged discussions about "non-human-aligned systems" seem divorced from concrete policies. Japan only briefly alludes to such dangers before shifting back to practical AI ethics.
This contrast reflects an interesting cultural divide in how the regions perceive existential threats. Japan intimately understands natural disaster risks, while "AI safety" resonates strongly in the UK research community. Still, the most successful plans likely address both immediate and far-off hazards.
Requests for Feedback vs. Definitive Actions
Throughout the UK strategy document, there are explicit requests for continued feedback from the AI community. It suggests piloting new programs, testing proposals and updating recommendations. The plan seems designed to evolve based on public and private input.
Japan's strategy, in contrast, reads as more authoritative and final. It states definitive positions on AI ethics, makes direct requests of companies, and outlines specific technology targets. There is little emphasis on open-ended collaboration or revisions.
The UK's participatory approach is admirable, but risks postponing real progress. Japan's assertiveness suggests confidence but could miss outside perspectives. Truly effective plans likely require decisive government leadership as well as mechanisms to incorporate external expertise.
Focusing Inward vs. Leading Globally
Unsurprisingly, both countries aim to establish international AI leadership. Japan outlines an expansive vision for multilateral cooperation, foreign investment, overseas talent programs and leading global AI governance. The UK similarly stresses attracting global talent and shaping international norms.
The UK, however, distinguishes itself by framing AI leadership as an essentially nationalistic goal, stating its aim to be the "best place to live and work with AI" with policies primarily benefiting its own citizens and businesses. Japan's rhetoric focuses outward, presenting plans to use AI to resolve pressing global issues like climate change.
This subtle distinction speaks volumes about the perceived role of technology leadership on the global stage. The UK strategy betrays a more competitive, nationalistic tone while Japan's thinks bigger. This may simply reflect cultural differences in diplomacy and tech policy. Regardless, true leadership requires both strong domestic capacity and open, constructive international engagement.
Key Takeaways
While not exhaustive, this analysis highlights some interesting strategic differences and lessons learned:
Play to your strengths, whether research excellence or industry pragmatism
Address both immediate, solvable risks and complex long-term challenges
Balance decisive government leadership with mechanisms to incorporate external expertise
Maintain a global outlook and collaborate internationally while building domestic capacity
For policymakers and executives, particularly those involved in AI compliance, I hope this example demonstrates the value of studying national AI strategies with a critical eye. Comparing plans can provide models to emulate and pitfalls to avoid on the road to AI leadership. With thoughtful strategy development and global collaboration, we can maximize the technology's benefits for every nation.