Artificial Intelligence and the Future of Governance

Artificial Intelligence (AI) is rapidly emerging as a transformative force in governance and public administration. Governments around the world are investing in AI to improve services and gain strategic advantages. A 2020 World Bank review noted that many governments view AI as a “strategic resource” to boost national competitiveness. In fact, over 50 countries have already developed or are developing national AI strategies. As AI capabilities advance, experts predict profound changes in how decisions are made and how power is exercised in society over the coming decades. This report examines how AI is predicted to shape governance structures in the near and long term, surveying expert predictions, current implementations, future models, and key challenges and concerns.

Emerging Trends in AI-Driven Governancesss

Governments have begun adopting AI across a range of functions, signaling a trend toward AI-assisted governance. Common applications include:

• Personalized public services and digital assistants: AI chatbots and virtual assistants help citizens navigate government services and information. For example, Singapore’s “Ask Jamie” virtual assistant handles queries across ~70 agencies, providing quick guidance to users through AI-powered chat and voice AI in government. Such tools offer more personalized, on-demand service delivery.
• Back-end automation and efficiency: Public agencies use AI to automate routine tasks (data entry, document processing) and optimize operations. This improves bureaucratic efficiency and frees up staff for higher-level work. AI systems are also analyzing large datasets to detect fraud (e.g. tax or welfare fraud) and ensure regulatory compliance.
• Data-driven policy and decision support: Early uses of AI in policymaking are appearing. AI can “sense patterns of need, develop evidence-based programs, forecast outcomes, and analyze effectiveness”, all of which fall in AI’s “sweet spot”. By rapidly crunching data, AI tools are helping officials make more informed decisions. For instance, city planners use AI to model traffic flows, and social services agencies use predictive analytics to allocate resources where needed.
• National AI strategies and investment: Recognizing AI’s potential, governments are making AI development a policy priority. AI is expected to add trillions to the global economy by 2030, and countries see leadership in AI as key to future prosperity and security. Policymakers are forming dedicated AI task forces, ethical guidelines, and regulatory sandboxes to both promote innovation and manage risks. This AI race is creating a new arena of international competition and collaboration.

Importantly, the pandemic further accelerated these trends, as governments sought tech solutions for remote services and data analysis. However, while industrialized nations and digital leaders forge ahead, adoption remains uneven globally – many developing countries lack the digital infrastructure or skills for advanced AI, raising concerns of widening inequality between “AI-rich” and “AI-poor” states .

Current AI Implementations in Governance: Case Studies

Real-world examples illustrate how AI is already being implemented in governance, with both promising results and cautionary tales:

Digital Government Pioneers (Estonia, Singapore, Finland)

Some small nations have become testbeds for AI in the public sector. Estonia is a leading user of AI solutions in public services – the government has dozens of AI projects ranging from automated document translation to traffic management. Estonia is even developing “Bürokratt,” a Siri/Alexa-like AI assistant that will allow citizens to access most government services via voice commands. The aim is a one-stop, AI-powered interface for everything from renewing passports to applying for benefits. Singapore similarly employs AI chatbots (Ask Jamie) and data analytics as part of its Smart Nation initiative. Finland launched AuroraAI in 2018 to provide citizens proactive, personalized e-services for healthcare and beyond. These cases show how democracies are using AI to make government more user-friendly and efficient.

AI in Law Enforcement and Justice

Police and judicial systems are cautiously experimenting with AI. In the United States, nearly half of federal agencies had “experimented with AI” by 2020, applying it to tasks like surveillance monitoring, suspect screening, and case prioritization . For example, several U.S. cities deployed predictive-policing algorithms to identify crime “hotspots” or individuals at risk of re-offending. However, many of these tools have proven faulty or biased, triggering public backlash. A notorious case was the UK’s 2020 attempt to algorithmically moderate exam scores when COVID-19 canceled exams – the AI system lowered nearly 40% of students’ grades and was found biased against those from disadvantaged schools. The outcry forced its abandonment. In the Netherlands, an algorithmic system (SyRI) designed to flag welfare fraud was ruled unlawful by a court for violating privacy and human rights.

AI in Public Services and Welfare

Beyond security, AI is also managing social programs. Many U.S. state agencies now use AI to help determine eligibility for benefits, detect welfare fraud, and allocate housing assistance . While automation can speed up service delivery, errors can have severe human impact – for example, automated systems have wrongly terminated or denied benefits, leading to false fraud accusations against innocent individuals . These incidents underscore the need for transparency and human oversight when AI intersects with citizens’ basic rights and entitlements.

Together, these case studies reveal a dual reality: AI offers innovative tools to improve governance, but if implemented poorly it can also cause harm. Lessons learned from early deployments are informing guidelines for more responsible use going forward.

Potential Future Models of AI-Influenced Governance

Looking ahead, experts envision several models for how governance might be transformed as AI capabilities mature. These scenarios range from AI being tightly controlled at the center of power, to AI and humans sharing decision-making, to radically decentralized systems. The likely outcome may blend elements of each, but examining them in turn provides insight into possible futures:

1. Centralized AI Governance

In a centralized AI governance model, AI systems are given broad, top-down authority to make decisions at the highest levels of government. One extreme vision is a future “AI ruler” – effectively an AI acting as an autocratic decision-maker, optimizing government operations with superhuman analysis. While no country has gone this far, some technocrats and futurists have speculated about “AI presidents” or algorithmic technocracies that could supposedly govern more efficiently than fallible humans. For example, in 2018 a Tokyo mayoral candidate ran on a platform of delegating city management to an AI, promising unbiased and efficient administration. Proponents argue a sufficiently advanced AI might make more rational, long-term decisions than politicians driven by short election cycles or personal interests.

However, centralized AI governance raises obvious dangers. Without human checks, an all-powerful AI could make decisions that lack empathy, ignore minority rights, or pursue harmful goals due to programming flaws. There is also risk of power concentration – control of the AI (by whoever programs or directs it) could create a new elite. Historian Yuval Harari warns that AI technology, if unchecked, “will further concentrate power among a small elite”, potentially eroding democracy and equality . A “benevolent AI dictator” is still a dictator; such a system might deliver services efficiently yet suppress freedoms. Most experts believe human oversight would remain essential even if AI handles many governing tasks . Thus, fully centralized AI governance is viewed as a risky, dystopian scenario unless strong safeguards are in place.

2. Hybrid Human–AI Governance

A more widely anticipated model is hybrid governance, where AI is deeply integrated into government processes but humans remain in the loop for oversight and final judgments. In this scenario, AI functions as an advisor, tool, and administrative workforce – augmenting human decision-makers rather than replacing them. Many democratic governments explicitly pursue this approach: leveraging AI’s strengths (data processing, pattern recognition, predictive analytics) to support human officials, who provide contextual judgment, ethical reasoning, and accountability.

In a hybrid model, one might imagine a future cabinet meeting where AI systems present evidence-based policy options, simulations of outcomes, and real-time public sentiment analysis, while elected leaders debate values and make the ultimate choice. Centralized decision-making is still done by people, but those people lean heavily on AI-generated insights. This could make governance more responsive and rational. Boston Consulting Group notes that AI can enable “a comprehensive, faster, and more rigorous approach to policymaking”, helping governments become “more responsive and [leaving] no one behind” . Routine administrative rulings (e.g. approving permits or benefits) might be automated to speed service, with humans handling exceptions or appeals. Crucially, hybrid governance demands a strong framework of responsible AI use – including transparency, fairness, and human accountability for AI-driven decisions . Many current AI ethics guidelines (such as the OECD and EU principles) aim to facilitate this balance: use AI to assist, not replace, human governance. In practice, hybrid systems are already emerging (as seen in earlier case studies), and they are likely to expand as the default model in most democracies, combining efficiency with oversight.

3. Decentralized AI-Based Governance

A more radical departure from traditional governance is the concept of decentralized, AI-enabled governance. This model envisions decision-making authority distributed across networks or communities, often via blockchain and AI technologies, rather than concentrated in a central government. One example is the rise of Decentralized Autonomous Organizations (DAOs) – member-run groups governed by transparent rules encoded in smart contracts . DAOs operate on blockchain platforms, using algorithms to enforce decisions voted on by members, effectively eliminating the need for a central executive. Futurists suggest that in the coming decades, we may see DAO-like structures tackling governance tasks at larger scales (for instance, managing a city’s resources or even aspects of national governance) in a democratic, bottom-up way. AI could play a role by helping these decentralized networks make sense of complex issues and coordinate collective action. For example, AI bots might facilitate community deliberation or automatically implement group decisions.

Proponents argue that decentralized AI governance could be more transparent and inclusive, as rules and decision logs are openly recorded on blockchains and open to public scrutiny . It might also be more resilient, avoiding single points of failure or corruption. However, significant challenges exist: such systems face scalability issues, security risks, and unclear legal status . They also require an active, digitally literate citizenry to function effectively. While DAOs today are mostly used in niche areas (like managing cryptocurrency funds or online communities ), they represent a potential template for decentralized governance. Some experts foresee hybrid models here too – for instance, local communities using DAO frameworks for participatory budgeting, while a central government AI coordinates broader policy. Decentralized governance is still experimental, but it embodies the idea that AI and automation might enable “self-governing” communities with less reliance on traditional hierarchies.

Ethical and Social Implications of AI-Driven Governance

The advance of AI in governance brings not only technical and structural changes, but also a host of ethical and social considerations that must be addressed. Key among these are:

Transparency and Explainability

AI algorithms, especially complex machine learning models, can be “black boxes” – their inner workings opaque even to their creators. In governance, this opacity is problematic. Citizens have a right to understand how decisions affecting them are made. A UN report emphasizes that “ensuring transparency and interpretability in public decisions relying on AI is essential for upholding public trust and fostering accountability.” If an AI system denies someone social benefits or flags them as a security risk, the individual and oversight bodies should be able to inspect the basis for that decision. This has spurred efforts in AI explainability and algorithmic transparency, such as requiring agencies to publish information about the data and rules an AI uses. Several jurisdictions are considering laws mandating impact assessments or “algorithmic transparency reports” for government AI systems. The goal is to avoid a future where governance by AI becomes an inscrutable technocracy alienated from the public it serves.

Accountability and Oversight

Who is responsible when an AI system makes a flawed or harmful decision? This question of accountability is central. Democratic governance rests on the principle that those wielding power can be held to account. With AI, accountability can blur – developers, vendors, and officials might all dodge responsibility, blaming the algorithm. To prevent this, experts call for clear accountability frameworks. Governments must establish that human authorities are always accountable for AI-driven outcomes . This may involve keeping a “human in the loop” for final decisions, or at least for review and appeal of automated decisions. It also requires oversight mechanisms: independent auditors or ethics boards to monitor government AI, and avenues for citizens to challenge algorithmic decisions (just as one can appeal a human bureaucrat’s decision). Without such measures, AI could enable an unaccountable bureaucracy where wrongs cannot be rectified – a scenario to be avoided at all costs.

Bias and Fairness

AI systems learn from data, and if that data reflects social biases or inequalities, the AI can perpetuate or even amplify those biases. Likewise, predictive models for policing or sentencing have been shown to disproportionately label minority or poor communities as high-risk, reinforcing discrimination. In governance, biased AI can lead to systemic injustice – e.g., unjustly denying loans or welfare, over-policing certain neighborhoods, or allocating resources unfairly. Ethical use of AI requires proactive bias mitigation: carefully curating training data, testing algorithms for disparate impacts, and involving diverse stakeholders in AI design. Some jurisdictions are exploring mandatory bias audits for government AI systems. Ultimately, AI should be used to reduce human bias in decisions, not entrench it. Achieving this is a major challenge that will shape public trust in AI-driven governance.

Privacy and Civil Liberties

The use of AI in surveillance and data analysis raises significant privacy concerns. AI can swiftly sift through personal data, CCTV feeds, social media, and more – potentially enabling mass surveillance on an unprecedented scale. Democratic societies fear the emergence of an “AI-powered Big Brother” that could monitor citizens’ every move. Without strict limits, AI surveillance could chill free speech and association (people act differently if they know an algorithm is watching). As noted, authoritarian regimes are already using AI in ways that “undermine democratic principles” and violate human rights . Even in democracies, law enforcement use of AI (like facial recognition in public spaces) has prompted protests over racial profiling and wrongful identifications. Policymakers are thus grappling with how to balance security and privacy in the AI age. Many recommend banning or tightly controlling uses of AI that enable pervasive surveillance, and ensuring any data collected for public purposes is handled with strong privacy protections. The principle of “privacy by design” is being urged for government AI projects, meaning privacy considerations are built into the system from the start. Safeguarding civil liberties in the face of powerful AI tools will be an ongoing ethical test for governance.

Loss of Human Judgment and Legitimacy

Another subtle social implication is the potential erosion of human agency in governance. If citizens come to feel that impersonal algorithms – rather than accountable representatives – are calling the shots, public alienation could grow. Scholars have warned of declining government “responsiveness” if AI decision-making replaces the back-and-forth of human deliberation . Important values like compassion, mercy, or common sense in unique cases might be hard to embed in code. Ensuring that governance remains “human-centric” and that AI is used to augment (not replace) human judgment is seen as vital . This also speaks to democratic legitimacy: people need to feel they have control and input, rather than being ruled by machines. Hence, maintaining avenues for human input – whether through public consultations, human review panels, or participatory AI systems – will be important to preserve the social contract.

The Role of AI in Key Governance Domains

AI’s impact on governance will play out across multiple domains of government activity. Three key areas where AI is already making inroads – and is poised to do more – are policymaking, law enforcement, and citizen engagement.

1. AI in Policy-Making and Administration

Policymaking has traditionally been as much art as science, but AI promises to inject more data-driven science into the art of governing. Governments are beginning to use AI for policy analysis, running simulations and predictive models to forecast the outcomes of proposed programs. For example, AI can help budget planners estimate economic impacts of a tax change, or help health officials model the spread of a disease under different intervention strategies. Machine learning algorithms can detect patterns in public data that suggest emerging problems requiring policy action (such as spikes in unemployment in a region). Additionally, AI can assist in drafting and reviewing legislation or regulations by automatically compiling relevant information and even generating initial text based on best practices. Early trials with large language models (like GPT) have shown potential in generating policy drafts or summarizing public consultation inputs. Crucially, AI can also provide real-time feedback on policy implementation: IoT sensors and analytics might report instantly on whether a city’s new traffic policy is reducing congestion or if a social program is reaching its target demographic, allowing agile adjustments. All these contribute to evidence-based policymaking, where decisions are guided by data rather than gut instinct. As a Harvard report notes, “augmenting policymakers’ intelligence could be a significant asset in addressing complex global challenges.” However, policymakers must be trained to interpret AI outputs critically and ethically, ensuring that human judgment and democratic deliberation remain central in setting goals and values for policy.

2. AI in Law Enforcement and Public Safety

Police and security agencies are among the earliest adopters of AI, drawn by the promise of greater efficiency in fighting crime. AI is used for tasks like predictive policing, which analyzes crime data to predict where crimes are likely and allocate police patrols accordingly. It’s also used for facial recognition to identify suspects from street cameras or border crossings, and for automating the analysis of surveillance footage. There are clear potential benefits: AI can sift through vast CCTV feeds or digital evidence far faster than human officers, flagging incidents or persons of interest (e.g. missing children or wanted fugitives). AI-based systems can also assist investigations by finding patterns across databases (linking crimes by modus operandi, etc.). In some cities, gunshot detection AI listens for gunfire sounds to enable rapid police response. But the use of AI in law enforcement has proven highly controversial due to issues of accuracy, bias, and civil liberties. Predictive policing algorithms have been criticized for reinforcing existing biased policing patterns – for instance, if historical data is skewed by over-policing of minority neighborhoods, the AI will predict more crime in those same neighborhoods, creating a feedback loop. There is also the specter of pervasive surveillance: AI-driven tools like face recognition and AI-powered drones can track individuals’ movements, raising human rights concerns. In response, some jurisdictions (like several U.S. cities and European countries) have banned police use of facial recognition in public spaces, and are pressing pause on predictive policing until fairness can be ensured. Even so, the trend of AI in public safety is likely to continue, with a focus on using it in constrained, oversight-heavy ways. For example, AI might be used to scan for cyber threats or analyze forensic evidence in labs, which pose fewer direct civil liberty issues. Any expansion of AI in frontline law enforcement will require rigorous bias testing, transparency to the public, and mechanisms for individuals to challenge automated accusations. Law enforcement AI must align with constitutional values – otherwise it risks undermining the justice it purports to serve.

3. AI in Citizen Services and Engagement

One of the most visible impacts of AI in governance for everyday people is in the realm of citizen services. AI is powering virtual assistants, chatbots, and kiosks that help citizens interact with government more conveniently. We’ve already seen how Singapore’s Ask Jamie or Estonia’s upcoming Bürokratt provide a single conversational point of contact for a range of government services. This trend is expected to grow – imagine conversational AI agents that handle tax questions, assist in filing applications, or guide someone through starting a business, available 24/7 in multiple languages. Such AI agents can dramatically cut wait times and improve user satisfaction if done well. AI is also being used to make public information more accessible, using natural language processing to help users search dense regulations or open data. Another facet is personalization: AI can tailor services to individual needs, for example by proactively reminding a citizen about license renewals or suggesting benefits they qualify for, based on their profile (with privacy safeguards). Beyond service delivery, AI can enhance citizen engagement and participation in governance. Some cities have experimented with AI tools to moderate and summarize large-scale public consultations – for instance, using NLP to group thousands of citizen comments on a policy proposal into themes for officials to review. This makes it easier to include broad public input in decision-making. There are also concepts of AI-facilitated direct democracy: using secure platforms where citizens vote on issues or budget allocations, with AI ensuring integrity and perhaps helping individuals understand the issues via personalized education. While still emerging, these examples show AI’s potential to create more responsive, user-centric government. The flip side is ensuring no one is left behind – not all citizens are tech-savvy, so alternative traditional channels must remain. Moreover, heavy reliance on digital AI services requires closing the digital divide (universal internet access, digital literacy) to avoid marginalizing those without connectivity. If managed inclusively, AI can become a bridge between citizens and state, making daily interactions smoother and potentially renewing civic engagement by giving people new ways to be heard.

Possible Risks and Challenges

Despite its promise, AI-driven governance comes with significant risks and challenges that need proactive management. Key among these are:

Cybersecurity Threats and System Vulnerabilities

As governments integrate AI into critical functions, they become vulnerable to new kinds of cyber threats. AI systems themselves can be hacked, manipulated, or fed false data (through adversarial attacks) to produce malicious outcomes. For instance, an attacker might poison the training data of a government AI so that it consistently makes biased decisions, or trick a facial recognition system with specially crafted images. The more government relies on AI, the more an outage or sabotage of these systems could disrupt services – imagine if an AI controlling traffic signals or power grids is compromised. Cybersecurity becomes paramount, requiring robust encryption, testing, and fail-safes for AI systems. Additionally, AI can be used by malicious actors: cybercriminals and hostile nations may deploy AI for more effective phishing, misinformation, or attacks on public infrastructure. Governments will face an AI-enabled adversary landscape and must harden their defenses correspondingly. International cooperation may be needed to prevent AI-fueled cyber warfare. Simply put, trust in AI governance depends on its security, so significant investment in cybersecurity and resilience is non-negotiable.

AI Bias and Inequity

As discussed, AI systems can inadvertently perpetuate bias, leading to unfair treatment of certain groups. If these biases are not addressed, AI-driven governance could exacerbate social inequalities. A biased algorithm used in loan approvals might consistently favor certain demographics over others, or a healthcare AI might underserve minority populations because the training data lacked diversity. These issues could erode public trust and even violate anti-discrimination laws. The challenge is that bias can be subtle and technically complex to identify. Governments will need to implement rigorous bias evaluation protocols for any AI system used, possibly including external audits. There is also a risk of “algorithmic disenfranchisement” – if decisions that used to be made by humans (who might have some empathy or discretion) are now made by rigid algorithms, people who don’t fit the norm could be systematically disadvantaged. For example, a strict algorithm for welfare eligibility might not accommodate an unusual but deserving case that a human caseworker could recognize. Overcoming this means keeping human review as a backstop and designing AI with fairness as a core objective (sometimes even weighting decisions to correct historical biases). Ethicists often note that AI’s introduction should be accompanied by measures to ensure equity, or else it could further concentrate benefits among the already privileged. This is a societal challenge as much as a technical one, requiring diverse stakeholder input in AI policy design.

Concentration of Power and Technocratic Control

AI’s complexity and resource needs may lead to power imbalances. Developing and deploying advanced AI can be expensive and requires expert knowledge, currently concentrated in a few big tech companies and government agencies. There’s a risk that a small group of AI experts or organizations ends up wielding disproportionate influence over governance (either directly through operating government AI, or indirectly through controlling the tech and data). This centralization of power could diminish democratic oversight. For instance, if a government outsources AI systems to a private tech vendor, that company gains power over public decisions without accountability to citizens. Or within government, decisions might become driven by AI specialists that few understand, sidelining elected officials and the public. To counter this, transparency about AI procurement and operation is needed, as well as open algorithms where feasible. Some scholars even call for public ownership of certain AI infrastructure so it remains accountable . Moreover, concentration of AI capability on the world stage could shift geopolitical power – countries leading in AI might dominate those lagging behind. As Russian President Vladimir Putin remarked, “whoever leads in AI will rule the world”. This dynamic could heighten international tensions (an AI arms race) and concentrate global power in a few AI superpowers, unless international governance mechanisms step in. Thus, a key challenge is ensuring AI deployment doesn’t inadvertently create a new class of AI oligarchs or authoritarian advantages that undermine the egalitarian ideals of governance.

Public Acceptance and Legitimacy

A softer but crucial challenge is whether the public will accept AI’s role in governance. Scandals or failures (like the biased exam grading algorithm in the UK) can lead to a loss of faith in government AI systems. Building and maintaining public trust is essential; otherwise, even beneficial AI innovations might face backlash or non-compliance. Governments will need to engage in public education about how AI is used, its benefits, and its safeguards. Citizen engagement in the design of AI policies can also improve legitimacy (people are more likely to trust what they had a voice in shaping). Ultimately, AI-driven governance will only succeed if it operates for the people and with the people’s informed consent. Misuse or secretive use of AI will breed suspicion and resistance.

In tackling these challenges, experts emphasize a proactive approach: anticipating risks early, setting robust ethical guidelines, and updating laws and institutions for the AI era. As one UN advisory group put it, “comprehensive ethical frameworks” and regulatory oversight must evolve in parallel with AI advances to ensure this powerful tool is “shaped for good”.

Long-Term Impacts on Political Systems and Global Governance

In the long run, AI’s influence on governance could reshape fundamental political structures — from the nature of democracy and authoritarianism to the dynamics of international relations. While predictions vary, several plausible impacts are foreseen by researchers and futurists:

Democracy in the Age of AI

Will AI erode democracy, or reinvigorate it? On one hand, analysts warn that AI, especially when combined with big data, could “erase many practical advantages of democracy”. Democracies historically benefited from open information flows and collective wisdom, but AI might grant autocracies those same capabilities (or better) through centralized data analysis. Moreover, AI can be used to manipulate public opinion — advanced algorithms might microtarget propaganda or generate fake content (deepfakes, bot-driven misinformation) that undermines informed democratic debate. There is fear that AI-augmented populism and misinformation could make electorates easier to sway and elections less fair. Harari has argued that without intervention, AI and biotech could tilt the balance toward tyranny, as regimes use them to surveil and “hack” citizens, while democratic processes lag behind. On the other hand, AI could strengthen democracy if used to enhance citizen participation and government responsiveness. For example, AI systems might help digest citizen feedback (making direct democracy at scale more feasible), or identify policy solutions that maximize social welfare without partisan bias. There is also the concept of “augmented democracy” where voters are informed by AI-driven simulations (showing likely outcomes of policy choices) enabling more rational decision-making. A recent IMF paper by Landemore suggests AI could “usher in a more inclusive, participatory, and deliberative form of democracy”, even at global scales . In practice, the impact on democracy will depend on human choices: whether governments use AI to empower citizens or to control them. Democratic nations are already collaborating on guidelines (like the G7’s “Hiroshima AI Process” in 2023) to ensure AI use upholds human rights and democratic values. The long-term hope is that AI becomes a tool to reduce bureaucracy and inform citizens, making democracies more effective and agile – but vigilance is needed to prevent the dystopian alternative.

AI and Authoritarianism

As noted, authoritarian regimes have eagerly adopted AI for surveillance and censorship. The long-term question is whether AI will entrench autocracy or possibly destabilize it. In the near term, AI hands autocrats powerful instruments to monitor and micro-manage society, potentially creating an Orwellian state where dissent is nearly impossible. However, some futurists argue AI could also carry seeds of autocracies’ downfall. If regimes rely too heavily on AI, they might face new weaknesses – for example, savvy dissidents could trick or evade automated systems. Also, AI’s need for innovation and data may conflict with the closed nature of autocracies. Innovation thrives in free inquiry environments; autocracies that stifle freedom may fall behind in AI advancement in the long run, despite short-term gains. Additionally, as AI takes over more administrative tasks, authoritarian leaders might lose the support of bureaucratic elites (if AI replaces their jobs), altering power dynamics. Overall, though, the consensus is that AI currently boosts autocratic capabilities more than democratic ones, by supercharging surveillance and information control. To counter this, activists and democracies are exploring how to “fight back” with AI – for instance, using AI to detect deepfake propaganda or to secure communications among dissidents. The interplay of AI and autocracy will be a critical battleground for human rights in the 21st century.

International Governance and the Global Order

AI has become a focus of global competition, with major powers racing to harness its potential for economic and military advantage. Nations leading in AI development, such as the US and China, are likely to wield greater influence in setting international norms and could dominate key industries, prompting calls for cooperative global governance of AI to avoid conflict.

On the international stage, AI is poised to alter power relations and necessitate new governance structures beyond the nation-state. Militarily, AI is driving an arms race in autonomous weapons and cyber warfare capabilities – raising the specter of destabilizing conflicts unless global agreements are reached. Diplomatically, countries are vying to set AI standards and export their governance models: democratic nations push for rules ensuring AI is “human-centric and trustworthy” (as in the proposed EU AI Act ), while more authoritarian countries may promote a sovereign control model. This fragmentation is evident in a “mosaic of national policies and multilateral initiatives” rather than a unified global approach . There are growing calls for a global governance framework for AI, akin to climate change or nuclear arms, since AI’s impacts cross borders. In 2023, the UN Secretary-General convened a High-Level Advisory Body on AI, which recommended a “globally inclusive and distributed architecture for AI governance based on international cooperation.” Ideas include an international agency to monitor superintelligent AI development, or treaties banning certain AI applications (e.g., an agreement not to use AI for social scoring that violates human rights).

However, achieving consensus is challenging given differing values and the competitive stakes involved. AI could also influence global governance by enabling better coordination on global problems – for example, AI models helping all countries manage climate risks, or optimizing supply chains to reduce hunger. If nations can collaborate, AI might strengthen international institutions by providing neutral analysis and fostering collective action guided by data. A futurist scenario imagines even a “global AI steward” that assists the UN or global councils in fairly allocating resources and mediating disputes, though this remains speculative. What is clear is that AI will be a double-edged sword internationally: it can either be “mutually reinforcing” through cooperation, or become an arena of rivalry, exacerbating inequalities and mistrust. The long-term global impact of AI on governance will depend on whether humanity can establish effective international norms and perhaps new institutions to guide the technology for the common good.