Wandering with the AI Revolution
This week at Geopolitics.Asia, we are not only utilizing AI for weekly trend monitoring, but also incorporating the “double iteration” scenario planning technique. We learned the double iteration technique from Adam Kahane’s convening Scenario Thailand workshop in 2013. This technique involves two iterations of (1) gathering input from workshop participants in a process we call “breathing in”, (2) categorizing the input information and generating driving forces using an inductive technique to produce four quadrant scenarios, and (3) using a deductive technique to develop narratives within each scenario and describe potential events. In the second iteration, we repeat the process using events generated from the first iteration, resulting in a final outcome of the scenario.”
The double iteration technique is more challenging than what we tackled last week, and while we have not yet been able to fully automate the process, the results are intriguing. During the first iteration, AI generated a scenario on IR and AI. In the second iteration, it shifted to a scenario that explores the interplay between the advancement of AI and economic development resulting from AI’s impact. We will go into more detail about each process later on. Although it is strictly under the human supervison, please note that this work is still in the development phase and should not be cited in public.
Breathing In
By utilizing AI to gather weekly trends, a trend radar, we have found that reducing the number of issues from over 50 to approximately 25 provides a clearer signal and makes the scenario planning process easier to handle. The AI has done a fine job of categorizing the outcome and weighting the information as follows:
The First Iteration
In the first iteration, we asked the AI to categorize and identify the driving forces from the trend radar (inductive method). The AI initially provided two categories: “politics and security” and “technology”. It then redefined the underlying causal driving forces (which should be observed at the level of mental model) as: for politics and security, sanctions and military tensions appear to be the most significant factors shaping events, and for technology, the development and advancement of artificial intelligence and machine learning appear to be the most significant factors. Thus, the four quadrant scenario and related events (deductive method) in each scenarios are as follows:
For Scenario 1: “Tense AI-Powered Military Standoff”:
- The deployment of autonomous weapons systems escalates international conflicts, leading to increased risk of accidental or unintended warfare.
- The use of AI in intelligence gathering and analysis leads to increased privacy concerns and violations of civil liberties.
- The race for AI superiority among nations intensifies, creating new security challenges and increasing the likelihood of cyberattacks and data breaches.
For Scenario 2: “Cooperative AI-Powered Global Security”:
- The international community collaborates to develop ethical standards and norms for the use of AI in military and intelligence operations, reducing the risk of conflict and escalation.
- The use of AI helps to improve intelligence gathering and analysis, leading to more effective and efficient counter-terrorism and cyber defense operations.
- The development of AI leads to new and innovative solutions for peacekeeping and conflict resolution, promoting stability and security in troubled regions.
For Scenario 3: “Limited AI-Powered Military Competition”:
- AI-powered weapons systems become more prevalent, leading to an arms race among nations and an increase in military spending.
- The development of AI in military and intelligence operations leads to increased economic competition and tensions among nations.
- The use of AI in military and intelligence operations raises new ethical and moral questions, leading to increased public debate and scrutiny.
For Scenario 4: “Passive AI-Powered Military Environment”:
- The development of AI in military and intelligence operations slows down, reducing the risk of conflict and escalation.
- The use of AI in military and intelligence operations remains limited, preserving privacy and civil liberties.
- The international community continues to lack a unified approach to the regulation and governance of AI, leading to inconsistent policies and practices.
The Second Iteration
The process we just completed is known as the first iteration of scenario planning technique. This involves using induction to identify two major driving forces from the input information, and then using deduction to develop related events within each scenario that emerges.
The AI has grouped the events it listed into six categories based on PESTLE analysis (Political, Economic, Social, Technological, Legal, and Environmental factors) and has again identified the two underlying causal driving forces to regenerate new and different four quadrant scenarios.
The events generated from the first iteration scenarios have been categorized as follows:
For Scenario 1: “Tense AI-Powered Military Standoff”:
- #1.1 (Politics, Technology)
- #1.2 (Social, Technology)
- #1.3 (Technology, Legal)
For Scenario 2: “Cooperative AI-Powered Global Security”:
- #2.1 (Politics, Technology)
- #2.2 (Technology, Legal)
- #2.3 (Politics, Social)
For Scenario 3: “Limited AI-Powered Military Competition”:
- #3.1 (Economics, Technology)
- #3.2 (Politics, Economics)
- #3.3 (Social, Legal)
For Scenario 4: “Passive AI-Powered Military Environment”:
- #4.1 (Technology)
- #4.2 (Social, Legal)
- #4.3 (Politics)
And then, the most interesting thing occurred. The AI then suggested two different driving forces: “The growing influence of emerging economies” and “The increasing use of renewable energy sources.” These two factors have the potential to significantly affect global politics, economics, and the environment, and shape the future in unique ways.
Now, I’ll explain the second iteration four quadrants scenario generated by AI in detail:
For the first scenario, “AI-Powered World of Collaboration and Cooperation” (Cooperative AI-Driven Economic Growth):
This scenario portrays a world where technology is rapidly advancing and political tensions are on the rise. The deployment of autonomous weapons has increased the risk of accidental or unintended warfare, causing nations to be constantly preparing for potential military action. AI’s use in intelligence gathering and analysis has raised serious privacy and civil liberties concerns, leading to a world where privacy is scarce and citizens’ rights are threatened. The competition among nations to gain an edge in AI use in military and intelligence operations has intensified, causing high levels of military spending and investment in tech research and development. Despite the challenges posed by the fast-changing tech landscape, the world continues to progress driven by the pursuit of military and economic power.
Drivers:
- Development of international ethical standards and norms for AI use in military and intelligence operations
- Cooperation between nations to enhance intelligence gathering and analysis using AI technology
Inhibitors:
- Lack of trust among nations and difficulty in reaching ethical standards agreement for AI
- Economic competition among nations resulting in conflicting priorities and limited resources for cooperation.
For the second scenario, “World of Collaborative AI” (Restricted AI-Driven Economic Competition), In this scenario, the world is characterized by strong international collaboration and cooperation aimed at ensuring the ethical and responsible use of artificial intelligence in military and intelligence operations. The effective deployment of AI in improving intelligence gathering and analysis has led to more efficient counter-terrorism and cyber defense operations. International standards and norms for AI use have been established, reducing the risk of conflict and promoting stability and security in troubled regions. The responsible use of AI is viewed as a means of promoting peace globally, with a growing recognition of the importance of international cooperation in this area.
Drivers:
- International cooperation: The international community works together to establish ethical standards and norms for AI in military and intelligence operations, leading to reduced conflict and increased stability.
- Advancement in AI technology: The development of AI brings new solutions for peacekeeping and conflict resolution and improves intelligence gathering and analysis.
Inhibitors:
- Lack of trust: The absence of trust among nations, particularly with regards to sensitive information sharing, may hinder collaboration and cooperation in establishing ethical standards for AI.
- Resistance to change: Some nations may resist the use of AI in military and intelligence operations as it raises new ethical questions and challenges traditional military strategies.
For the third scenario, “AI-Powered Global Order ,” (AI-Driven Global Economic Transformation) the world is characterized by a collaborative and empowered approach to artificial intelligence development and deployment. Global leaders and organizations come together to share expertise and resources, resulting in a coordinated and effective approach to AI technology and applications. The advancements in AI bring new innovations and opportunities for economic and social progress, which are seen across numerous industries. However, there are also challenges to overcome, such as concerns about the impact of AI on privacy, security, and society, leading to increased regulation and scrutiny.
Drivers:
- Increased global cooperation and collaboration in AI development and governance.
- Advancements in AI technology, leading to new innovations and applications in various industries.
Inhibitors:
- Resistance to change and fears about the impact of AI on society, causing political and social barriers to adoption.
- Privacy and security concerns around AI use, resulting in increased regulation and scrutiny.
For the fourth scenario, “Global Partnership for AI Peace and Prosperity”, (Passive AI-Driven Economic Environment) the international community has come together with the realization of AI’s immense potential to drive economic growth, solve global challenges, and enhance quality of life. It has become evident that responsible development and deployment of AI require global cooperation and collaboration.
As a result, nations are collaborating to establish a unified approach to AI governance and regulation, emphasizing trust, transparency, and ethical principles. This cooperation has overcome resistance to change, generated new opportunities for innovation, and instilled a more positive outlook for the future of AI.
AI has emerged as a significant driver of economic growth and social progress, creating new jobs, enhancing healthcare, and increasing access to information and services. At the same time, the international community is striving to guarantee that the benefits of AI are distributed equitably and that its negative impacts are mitigated through effective regulation and governance.
Overall, this scenario is characterized by a high degree of collaboration, cooperation, and trust, as well as a strong commitment to responsible AI development and deployment.
Drivers:
- Increasing recognition of the potential benefits of AI for society and the global economy
- Growing momentum for international cooperation and collaboration on AI governance and regulation
Inhibitors:
- Resistance to change and challenges in adopting new technologies and practices
- Political, economic, and ideological differences among nations and stakeholders on the approach to AI regulation and governance
Conclusion
The outcome of AI is fascinating, despite the fact that the work is not yet automated and requires close human supervision, albeit it seems to have more room to be improved. AI also suggests a best and worst case scenario, though the classification is subjective and depends on the values and priorities of stakeholders involved. For example, “Global AI-Powered Peace” could be considered the best case scenario in the context of this scenario planning exercise, while “AI-Fueled Global Tensions” could be considered the worst case scenario. However, this depends on the specific criteria used to define “best” or “worst,” and it doesn’t relate to the generated scenario in this experiment.
