[Blog]AI in UK Immigration Control: The Hidden Risks Behind Administrative Efficiency

2026-10-31

In October 2026, the UK-based digital rights organisation Open Rights Group (ORG) published a report entitled "Automated Discrimination: Unpacking the Digital Hostile Environment." The report examines the rapid expansion of artificial intelligence (AI) in immigration administration and border enforcement, warning that technologies introduced in the name of efficiency may institutionalise discrimination and undermine fundamental human rights.

The report's significance extends beyond identifying technical weaknesses in AI systems. It challenges the ideology of "techno-solutionism," which treats migration as a technical problem that can be solved through algorithms. This raises fundamental questions that are equally relevant to Japan's immigration policies and the future of digital public administration.

From Asylum Decisions to Predictive Border Surveillance

According to the report, AI is already being tested or deployed in several areas of the UK's immigration system, including age estimation for asylum seekers, summarisation of asylum interviews, analysis of country-of-origin information, and immigration case prioritisation. One particularly concerning example is the Identify and Prioritise Immigration Cases (IPIC) system, whose decision-making processes remain insufficiently transparent.

Other applications include the examination of mobile phones belonging to irregular entrants, automated identification of potentially fraudulent marriages, live facial recognition, AI-powered drones, satellite surveillance, and autonomous border-monitoring technologies.

These developments represent a shift from responding to established immigration violations toward predicting and preventing perceived future threats. However, a prediction is not proof. Statistical correlations and algorithmic risk assessments cannot, by themselves, establish that an individual has committed an immigration violation. When administrative authorities fail to maintain this distinction, automated classifications may begin to outweigh individual circumstances and procedural fairness.

How Automated Discrimination Becomes Institutionalised

AI is often presented as an objective alternative to human judgment. Yet algorithms trained on historical data may reproduce existing inequalities, discriminatory practices, and institutional biases. Because automated decisions appear technically neutral, their discriminatory consequences may become more difficult to identify and challenge.

The ORG report also criticises the lack of transparency surrounding government AI systems. National security justifications have reportedly been used to restrict access to information, making independent scrutiny difficult. Furthermore, reliance on surveillance technologies supplied by foreign defence and security companies raises concerns about technological dependency and the government's ability to exercise meaningful control over its own digital infrastructure.

If public authorities cannot explain how an AI-assisted decision was reached, accountability becomes uncertain. Who is responsible for an incorrect or discriminatory outcome: the technology provider, the administrative officer, or the institution that authorised the system? This is not merely a technological question. It is fundamentally a matter of administrative law, democratic oversight, and public accountability.

The Balanced Coexistence Model: Explainability as a Fundamental Principle

The Balanced Coexistence Model (BCM) that I propose seeks to establish a sustainable framework for immigration policy by balancing the rights and responsibilities of foreign nationals, employers, administrative authorities, and local communities. Rather than relying primarily on numerical restrictions or stronger enforcement, BCM emphasises institutional accountability, mutual obligations, and transparent governance.

One of its central principles is Explainability. Administrative authorities must be able to explain the evidence, criteria, and reasoning underlying their decisions. This obligation remains essential when AI is involved.

For example, AI may be useful in detecting inconsistencies in immigration applications. However, identifying a potential inconsistency is fundamentally different from establishing grounds for refusal. Applicants must have meaningful opportunities to correct errors, provide explanations, and submit additional evidence. The administrative authority must retain responsibility for the final decision.

From the BCM perspective, the central question is therefore not simply whether AI should be introduced. It is how its functions, limitations, oversight mechanisms, and procedures for challenging incorrect decisions should be incorporated into the institutional framework.

Connectivity and Preventive Governance: The Positive Potential of AI

BCM does not reject digitalisation. On the contrary, properly designed technologies can reduce administrative burdens, improve transparency, and strengthen the relationship between foreign residents and public institutions.

For example, secure information exchange among immigration authorities, financial institutions, insurance providers, and employers could reduce repetitive document submissions and verification procedures. This reflects BCM's principle of Connectivity. Such integration, however, must be accompanied by appropriate authentication, informed consent, purpose limitations, data minimisation, and auditable access controls.

Another important principle is Preventive Governance. Rather than concentrating exclusively on detecting and punishing violations after they occur, administrative systems should help prevent mistakes and encourage lawful compliance from the beginning.

AI could support this objective by identifying missing documents, checking application formats, providing multilingual guidance, explaining regulatory changes, and helping applicants understand their obligations before submitting applications.

This approach differs fundamentally from predictive immigration enforcement. Preventive assistance seeks to help individuals avoid procedural errors and legal violations. Predictive enforcement, by contrast, may subject individuals to heightened surveillance based on assumptions about their future behaviour. Although both approaches may employ AI, their institutional purposes and consequences are substantially different.

Mutual Obligations and Accountability in Digital Administration

BCM emphasises that obligations must be reciprocal. Foreign nationals are expected to comply with immigration laws and participate responsibly in society, while governments and employers must also fulfil corresponding responsibilities.

A system that demands accurate information and lawful conduct from migrants while providing opaque decisions, inadequate explanations, or ineffective correction procedures cannot be considered balanced.

When administrative authorities deploy AI, they should be responsible for verifying its accuracy, assessing discriminatory impacts, explaining relevant decisions, correcting errors, and maintaining effective remedies. Outsourcing technology to private companies must not mean outsourcing public accountability.

Immigration and asylum decisions can profoundly affect employment, family relationships, personal security, and even an individual's life. Increasing the number of processed applications cannot be regarded as genuine administrative improvement if procedural fairness, accuracy, and access to remedies are simultaneously weakened.

Lessons for Japan's Immigration Administration

The UK experience offers important lessons for Japan as immigration procedures become increasingly digitalised and the potential use of AI in administrative decision-making expands.

Before introducing AI-assisted examination systems, authorities should clearly define their purposes, permissible functions, and limitations. Algorithmic recommendations must remain distinguishable from legally accountable administrative decisions.

Applicants should be able to correct inaccurate information, understand the basis of decisions affecting them, and obtain meaningful review where errors occur. Information sources and decision-making processes should be traceable, while independent evaluation should be possible.

These safeguards should not be treated as optional features to be added after technological deployment. They should be incorporated into the original institutional design.

Furthermore, the benefits of digitalisation should not be confined to administrative authorities. If AI reduces processing costs and improves operational efficiency, those benefits should also reach applicants and employers through shorter waiting periods, fewer documentary requirements, clearer procedures, and greater predictability.

Conclusion: The Real Question Is Not AI, but the Purpose of Governance

The Open Rights Group report challenges the assumption that increasingly sophisticated technology can resolve immigration challenges through stronger surveillance and automated enforcement. It also warns that criticising AI alone is insufficient if the underlying political framework continues to treat migration primarily as a threat.

This analysis is substantially consistent with the Balanced Coexistence Model. However, BCM does not advocate the wholesale rejection of AI. Instead, it proposes that technology should operate within an institutional framework grounded in Explainability, Connectivity, Preventive Governance, and Mutual Obligations.

Is digitalisation intended to monitor, classify, and exclude foreign nationals? Or is it intended to create a more transparent, accountable, and accessible system in which migrants and receiving societies can fulfil their respective responsibilities?

The distinction depends less on the sophistication of algorithms than on the principles guiding their use.

As governments increasingly adopt AI, the essential questions are not only what technology can accomplish, but also what public authorities should do and what they must refrain from doing. The Balanced Coexistence Model offers a framework for addressing these questions and ensuring that digital transformation contributes to fair, sustainable, and mutually responsible coexistence rather than automated discrimination.