In recent years, the influence of technology on governance has escalated dramatically, evolving into what some experts are calling “AI governance.” This shift raises critical questions about the implications of automating decision-making, particularly in high-stakes environments such as warfare and public administration. The impact of these advancements, as illustrated by tragic events and controversial implementations, prompts both concern and intrigue.
One of the more alarming developments occurred during military operations, where AI systems, lacking human oversight, contributed to devastating outcomes. John Ruehl highlights the tragedy at the Minab school, where AI-enabled targeting led to the deaths of over 150 children. Scott Ritter, with his military expertise, has emphasized the necessity of human review in military target selection. The technology, though touted as innovative, has shown significant flaws in execution, evident in poorly targeted strikes even against decoy targets.
Ruehl notes that AI’s reach is not limited to military applications; it extends into civilian sectors, including welfare, law enforcement, bail decisions, and healthcare evaluations in the U.S. Notably, Thailand serves as a case study in the integration of AI into governance systems. The country, with its reliance on tourism and expatriate residents, is evolving towards a “Data-Driven Smart City” model anchored by the upcoming Thai Digital Arrival Card (TDAC). This shift is designed to streamline data collection and enhance AI-driven verification systems by 2026.
As a legal consultant who has spent over a decade monitoring the trajectory of Thailand’s public sector data governance, I view the transitions set for 2026 not merely as a technological upgrade, but as a “Sharp” pivot toward Integrated Big Data Administration. We are moving beyond the era of fragmented paper trails into a sophisticated regime of Data Sovereignty, where inter-agency synchronization dictates the legal standing of every expatriate in the Kingdom.
The nationwide mandate of the TDAC (Thailand Digital Arrival Card), effectively replacing the physical TM6 as of May 1, 2026, is the cornerstone of this reform. It represents a “Data Cleaning” initiative at the source, designed to feed high-fidelity information into AI-driven verification systems…
Pattaya, as a premier global expat hub, is leading this transition into a Data-Driven Smart City. As we approach May 2026, compliance is no longer a suggestion; it is a digital prerequisite. Managing your data with the same rigor as your finances is the only way to ensure a secure and long-term future in the Kingdom.
The language used in such initiatives can be filled with technical jargon, which may obscure the challenges inherent in achieving their ambitious goals. For instance, the integration of health data may primarily reflect the public healthcare system, which has its own issues, particularly in catering to expats who typically rely on private hospitals. Furthermore, the high level of indebtedness in the public system can largely be attributed to the strain of treating tourists injured in accidents.
By John P. Ruehl, an Australian-American journalist living in Washington, D.C., and a world affairs correspondent for the Independent Media Institute. He is a contributor to several foreign affairs publications, and his book, Budget Superpower: How Russia Challenges the West With an Economy Smaller Than Texas’, was published in December 2022. Follow him on X @john_ruehl. Produced by Economy for All, a project of the Independent Media Institute
In April, the General Services Administration announced plans to automate 1 million work hours annually after cutting nearly 40 percent of its staff since October 2024. Similar reductions have also been observed across the government workforce.
While the Elon Musk-led Department of Government Efficiency (DOGE) may have diminished as an official program, it continues to hire personnel who have been working across several agencies, effectively accelerating further government automation.
Washington first integrated extensive automation during World War II to handle substantial military data. The practice later expanded into the postwar administrative environment. Unlike earlier phases, however, current AI-driven automation is eliminating job roles across both public and private sectors without establishing equivalent replacements.
These systems are already influencing crucial governmental functions tied to state authority and legitimacy, including military operations. Reports on the Pentagon’s Maven Smart System, employed in the 2026 Iran conflict, reveal the extent of technological advancement in this area.
Launched in 2017, Maven is a network of contractor-developed systems, spearheaded by Palantir Technologies with contributions from companies like Microsoft and Amazon. It integrates satellite imagery, drone feeds, radar, infrared sensors, and signals intelligence, combined with numerous other data sources. Advanced computer vision algorithms classify “battlefield objects” and utilize an “AI Asset Tasking Recommender” for targeting suggestions.
Previously, such tasks required thousands of personnel; advancements now allow a limited number of operators to perform them within seconds. Targeting output soared from fewer than 100 prior to Maven to over 5,000 daily amidst the Iran war, as highlighted by a National Geospatial-Intelligence Agency official in Wired.
Earlier iterations of Maven have been utilized in Afghanistan, Ukraine, Iraq, Syria, and Yemen, and during the controversial seizure of Nicolás Maduro in Venezuela. While not entirely autonomous, the technology represents a progression toward genuine agentic AI warfare, where systems function with minimal human intervention.
The Pentagon is now seeking $54 billion for its 2027 budget to advance “autonomous and remotely operated systems” across air, land, and sea, including the proposed Drone Dominance program. This request signals a clear intention to reduce human roles in military operations, particularly as troop numbers continue to decline, with a 64% reduction observed from 1968 to 2025. The adoption of loitering drones by Azerbaijan in Armenia in 2020 and AI-enhanced wartime methods by Israel in Gaza exemplify how swiftly nations can implement such systems. Russia and China are also vigorously enhancing their autonomous capabilities, often outpacing U.S. advancements.
Minimizing human deliberation in military actions compresses legal scrutiny tied to international humanitarian law, which is grounded in the Geneva Conventions and their Additional Protocols. “[T]he opacity of modern AI makes it… harder to trace who is responsible for errors, and thus secure justice for victims,” cautioned the Lieber Institute. This ambiguity compromises accountability and enforcement, illustrating the inadequacies of existing legal frameworks when confronting AI-driven targeting methodologies.
The principles of distinction, proportionality, and precaution are increasingly strained by emerging AI weaponry, with global governmental enthusiasm for additional regulation waning as they embrace reduced human oversight to gain competitive advantages.
All-of-Government Approach
The transition towards AI systems carries significant domestic ramifications. Core state functions, including law enforcement, legal processes, and public service administration, are increasingly automated, often with growing degrees of autonomy.
Proponents argue these systems may reduce human error and political biases, enabling quicker, more consistent decisions that foster improved governance and infrastructure. Lawmakers are urged to align with the private sector, which has rapidly integrated automated and autonomous technologies to enhance efficiency and competitiveness.
An example of this innovation can be found in Albania, where the AI-generated virtual ‘minister,’ Diella, was introduced under Prime Minister Edi Rama’s administration to combat corruption. Her opening speech in parliament in 2025, powered by OpenAI models, drew significant international attention. While domestic support for this move is mixed, it serves as a public iteration of AI governance, sparking normalization discussions. However, concerns persist regarding transparency about the data used and those responsible for maintaining it, as noted by experienced IT professional Besmir Semanaj in an interview with Deutsche Welle.
Since the 1990s, law enforcement agencies globally have adopted predictive and discriminative AI, utilizing personal data to generate risk scores for individuals and regions. In 2025, the British government revealed plans for a homicide prediction project aimed at identifying potential murderers, further illustrating the cutting-edge capabilities of firms like Palantir and Babel Street.
As automation expands, so does the autonomy of AI systems. Police robots—from Singapore’s patrol units to Miami’s autonomous security vehicles—are adopting facial and vehicle recognition technologies to maintain surveillance across public spaces and swiftly alert law enforcement.
Automation and AI have also made significant inroads into the legal sector, influencing matters of individual liberty. In the United States, bail and sentencing decisions often depend on algorithmic tools, such as Arnold Ventures’ Public Safety Assessment, which analyzes several objective factors to predict defendants’ behaviors. Similar functions are performed by AI tools like COMPAS, PRIME, and HARMLESS.
Concerns surrounding these systems were highlighted by the Michigan Joint Task Force on Jail and Pretrial Incarceration, which raised issues regarding the reliability of Arnold Ventures’ assertions and the potential harm associated with using historical criminal data in risk assessments.
AI applications also extend to family law matters, including divorce settlements. The Australian software, Split Up, developed in the 1990s, later inspired tools like Amica, backed by the government to help determine asset division based on financial input and precedents.
Similarly, Brazil’s Victor Program aids the Supreme Federal Court in quickly categorizing cases using document analysis and natural-language processing. In China, the implementation of “smart courts” is even more advanced, enabling AI utilization in document drafting, evidence organization, and case assessments. This enhances decision standardization at the expense of human discretion. Other countries, including Canada and the UK, have enacted measures to permit AI in case management, though strictly prohibiting its use in essential judicial determinations.
While implementing automation is often simpler for smaller states and cities, Estonia stands out as a pioneer in this field. The nation has begun integrating automation into its judiciary, including AI-assisted judges for small claims disputes. The e-Estonia platform facilitates the delivery of state benefits, often without the need for citizen applications. Estonian Prime Minister Kristen Michal characterized these AI systems as “predictive, personalized, and proactive.”
Understanding the Risks
AI-driven governance is closely linked with initiatives such as Smart Cities, 15-minute cities, and various forms of social credit systems, which integrate public infrastructure and administration through automated management. In 2025, Palantir CEO Alex Karp and corporate counsel Nicholas W. Zamiska advocated for stronger ties between Silicon Valley and government in their publication, The Technological Republic.
While the administrative apparatus may be reducing its workforce, the automated systems taking its place create more complex and intrusive governmental structures. By entrusting public authority to private technology firms and relying on opaque algorithms instead of identifiable representatives, citizens are understandably uneasy. A 2025 article from Cornell Brooks Public Policy illustrated mixed perceptions in the U.S. regarding AI’s role in governance, particularly when it comes to high-stakes decisions.
The techniques crafted to manage society can also be weaponized by other actors. In 2025, Anthropic reported an incident where a Chinese state-sponsored hacker utilized its Claude agentic AI to target 30 entities globally, including tech firms, government bodies, and financial institutions, successfully infiltrating several. The company described it as the first major cyberattack executed with minimal human involvement.
Administrative failures resulting from automation have surfaced serious concerns for years. In the Netherlands, a self-learning system deployed by the tax authorities erroneously penalized thousands of families—predominantly from marginalized backgrounds—leading some to financial despair and even custody loss. In 2016, Arkansas implemented automated Medicaid assessments that abruptly terminated support for vulnerable recipients, igniting federal legal challenges. The Department of Homeland Security has also frequently misidentified individuals during automated screenings, preventing travel for many. In Colorado in 2020, an automated license plate reader falsely flagged an innocent mother’s car, prompting police to detain her and her children at gunpoint.
Automated systems can normalize decision-making processes that undermine context. Research from a Technical University of Munich project on algorithmic governance has shown that oversimplified decision-making can lead to a disconnect from nuanced human judgment. As reliance on “algorithmic truth” increases, the intricate reasoning of human discretion becomes marginalized.
The potential for heightened censorship and political manipulation also escalates with automation. Embracing automated governance means relinquishing some degree of human oversight in self-administration. Collective governance, reliant on public discourse and accountability, risks erosion in favor of opaque structures that are difficult to scrutinize and comprehend.
Regulation for New Governance
Regulation is struggling to keep pace with rapid advancements, although the EU’s General Data Protection Regulation (GDPR) and the Digital Services Act and Digital Markets Act offer some regulatory framework. Initiatives like the Open Government Partnership are also advocating for international regulations on AI and automation.
Regulatory initiatives appear less stringent in other regions. The Transparent Automated Governance (TAG) Act has established rules for U.S. federal agencies, yet Washington largely adopts a market-focused approach, with state and local governments taking a more proactive stance in establishing AI regulations. Similarly, China has prioritized experimentation over a comprehensive regulatory framework.
The collaboration between government and Big Tech has sparked contention, particularly in military contexts. Anthropic’s concerns regarding the use of its Claude AI model in Maven-related activities led to U.S. officials deeming it a “supply-chain risk,” prompting subsequent lawsuits from the firm. Google had previously withdrawn from its own Maven contract amid employee protests in 2018, yet clandestine collaboration has persisted.
Consequently, governments are compelled to develop these capabilities in-house. A 2019 Stanford Report entitled “Government by Algorithm” indicated over half of algorithm applications are developed internally, signifying a substantial creative drive within agencies. However, matching the pace of the private sector remains challenging. An Emory Law Journal study warned that “mounting evidence” indicates agencies may rely on systems where they lack expertise, which restricts discretion and nuanced reasoning.
There is little indication that AI governance will slow its advance. Having reshaped much of the private sector, the American Academy of Arts and Sciences predicts a further expansion into “back-end decision-making,” still primarily overseen by human officials.
Given this trend, citizens must adopt their own strategies to navigate increasingly AI-guided governance. Automated systems have demonstrated their capacity to challenge both governmental bureaucracy and private-sector oversight. The widely-used DoNotPay AI chatbot has successfully contested hundreds of thousands of parking tickets in the U.S. and UK by streamlining legal appeals. As governmental processes become more automated and impersonal, the public may need to view automation as a tool to navigate and, when necessary, defend against bureaucracy rather than merely acquiescing to it.
