Can AI Improve Government, or Is It a Bureaucratic Shortcut?

BUSAN, South Korea - For decades, governments have relied on bureaucracy, legal frameworks, and human judgment to administer public services, regulate economies, and shape urban development. Now, with artificial intelligence embedding itself into public administration, a new paradigm is emerging—one that promises efficiency, precision, and data-driven decision-making. Yet, beneath the surface of this technological revolution lies a deeper, more troubling question: Is AI making governance more accountable or merely shifting responsibility away from humans?

In Busan, South Korea’s second-largest city, this debate is playing out in real time. Recently, Busan became the first metropolitan city in the country to introduce ethical guidelines for public officials using generative AI. These guidelines aim to prevent misinformation, protect privacy, and ensure that human oversight remains intact. On the surface, it appears to be a forward-thinking policy designed to balance innovation with caution. But is it enough?

Globally, AI is being adopted at an unprecedented pace in governance. While its potential benefits—automating paperwork, optimizing resource allocation, and improving urban planning—are undeniable, so are the risks. Governments that fail to properly regulate AI risk creating unaccountable bureaucracies where decisions are influenced, or even made, by algorithms that no one fully understands.

Busan’s AI ethics guidelines are an attempt to prevent such a scenario. But as history has shown, well-intended policies can fail if they lack enforcement, clear accountability structures, and public transparency. If AI governance is not handled correctly, citizens could find themselves at the mercy of unexplainable, algorithm-driven decisions, with no clear path for appeal or correction.

Can Civil Servants Handle AI Responsibly?

One of the most immediate concerns with AI in governance is whether public officials are equipped to use it responsibly. Unlike AI researchers or data scientists, civil servants are not trained to understand the complexities of machine learning models. Most government employees have little to no experience in detecting AI bias, verifying AI-generated content, or evaluating the risks of automation.

This knowledge gap poses a significant threat. Studies have shown that when humans work alongside AI, they often fall victim to automation bias—placing too much trust in AI-generated outputs, even when those outputs contain errors. If public officials rely on AI without critically evaluating its recommendations, the consequences could be severe.

In the Netherlands, an AI-driven welfare fraud detection system falsely accused thousands of families of committing fraud, leading to financial ruin and a national political crisis. Many bureaucrats administering the system had no understanding of how the AI model worked, making it impossible for them to intervene when errors surfaced. The scandal resulted in the resignation of the Dutch government and remains one of the most infamous examples of AI governance failure.

A similar issue arose in the United Kingdom, where an AI algorithm used to predict student exam results disproportionately lowered the grades of students from disadvantaged backgrounds. The problem was not just the AI itself but the bureaucrats who blindly trusted it, failing to recognize its discriminatory effects until public outrage forced a reversal of the policy.

Busan’s AI guidelines attempt to address this issue by mandating human oversight in all AI-assisted decision-making. However, without proper training, will civil servants even know how to challenge an AI-generated recommendation? The policy assumes that government employees will take an active role in fact-checking and verifying AI content, but in reality, many may not have the confidence or expertise to do so.

Progress vs. Ethical Caution

Busan is not alone in its efforts to regulate AI in governance. Cities across the world are struggling with the same questions: How do you use AI efficiently while ensuring fairness, accountability, and public trust? The answers vary widely.

New York City has implemented one of the most comprehensive AI governance frameworks, requiring bias audits for AI hiring tools and public disclosure of how AI influences city policies. Meanwhile, Singapore has launched AI Verify, an audit tool that evaluates AI models for fairness, transparency, and security, ensuring that AI systems used in government adhere to strict ethical standards.

Tokyo has taken a different approach, focusing on data security and risk mitigation. Public officials using AI must sign agreements acknowledging their responsibility for AI-generated content and are required to undergo training before using AI in administrative tasks. The Tokyo government also restricts the use of generative AI in high-risk decision-making, ensuring that AI tools are used primarily for low-stakes tasks like document drafting rather than for policy enforcement.

Compared to these cities, Busan’s policy is a step in the right direction but lacks critical enforcement mechanisms. While it sets out ethical principles—fairness, trust, responsibility, and security—it does not yet outline how compliance will be monitored, whether AI-generated government decisions will be made transparent to the public, or how civil servants will be trained to ensure AI is used responsibly.

AI and the Economic Realities of Busan

Busan’s adoption of AI must also be viewed within the broader context of its economic and demographic challenges. The city has struggled to retain young talent, with many leaving for Seoul or overseas in search of better job opportunities. Busan’s GDP growth has stagnated, and its economic ranking has fallen behind that of Incheon, a city that was once considered less competitive.

AI is often marketed as a solution for boosting efficiency and modernizing bureaucracy, but in Busan, there is a risk that AI could exacerbate existing inequalities rather than solve them.

If AI is used to automate administrative processes, it could eliminate public sector jobs, leaving even fewer employment opportunities for young professionals in Busan. Additionally, if AI-driven governance is implemented unevenly—favoring wealthier districts for smart city initiatives while neglecting economically weaker areas—the gap between prosperous and struggling communities could widen.

Busan’s government must ask itself: Is AI being used to improve public services equitably, or is it simply a tool to cut costs and reduce administrative burdens? If economic and social disparities are not considered, AI could become a force that deepens regional inequalities rather than alleviating them.

The Danger of AI Bureaucracy

Beyond economic concerns, the integration of AI into governance raises a deeper philosophical question: Does AI make government more accessible, or does it push citizens further away from decision-making?

In a traditional democratic system, government decisions are made by elected officials and accountable bureaucrats. But as AI takes over administrative functions, decisions are increasingly influenced by opaque algorithms that lack human judgment, cultural awareness, and moral reasoning.

If citizens begin to feel that they are dealing with automated systems rather than human officials, public trust in government could erode. AI-driven bureaucracy runs the risk of becoming a faceless, unaccountable system where complaints go unanswered, appeals are handled by automated scripts, and citizens have little recourse when things go wrong.

Busan, and every city embracing AI governance, must ensure that AI serves the public rather than alienating them. This means keeping humans in the loop, making AI decisions transparent, and ensuring that technology is used to enhance governance rather than replace it.

The debate over AI in government is not just about technological advancement—it is about democracy, trust, and the balance of power between citizens and the state. Whether AI enhances governance or weakens it will depend not on how efficiently it is deployed, but on how responsibly it is controlled.