Two retired United States diplomats, Dr Don Kilburg and Virginia Blaser, who together bring more than sixty years of public service experience, examine this transformation in their new book AI Use Cases for Consular Affairs, published by Routledge. Their careers spanned tens of thousands of visa and passport decisions, large-scale overseas passport operations, fraud investigations, citizen evacuations and emergencies, and the practical introduction of new technologies into diplomatic work. That combination of operational experience and technical interest gives the book an unusually grounded perspective on a debate that is too often dominated either by technologists or by abstract ethicists.

The central shift the authors describe is subtle but profound. Governments can already use AI in consular work; the technology is deployed in identity verification, document authentication, biometric matching, multilingual assistance and application triage. The question that remains open, they argue, is normative rather than technical: where should the line be drawn between what a machine may prepare and what only a human may decide? Kilburg frames it directly, noting that the real issue is no longer capability but limits, asking what a machine should never be allowed to decide on behalf of the state.

The policy momentum behind adoption is strong. In September 2025, cabinet-level officials from Australia, Canada, New Zealand, the United Kingdom and the United States issued a joint communique calling for increased efforts to address irregular migration, document fraud and visa abuse, explicitly including the leveraging of advancements in technology such as artificial intelligence wherever possible. In practice, that means AI is playing a growing role in how applications are screened, how documents are checked and how potential risks are flagged before a traveller ever reaches a border checkpoint. Canada’s immigration department, for example, has published transparency material describing how advanced data analytics and automated triage support the processing of applications, reflecting a broader trend among Five Country partners toward institutionalized machine assistance in immigration pipelines.

Technically, the promise of these systems rests on pattern recognition at a scale no human team can match. Automated triage engines can classify large volumes of visa applications, identifying straightforward or lower-risk cases for streamlined handling while flagging others for closer scrutiny. Machine learning models can detect anomalies in document images, spot inconsistencies across biometric records, and surface patterns historically associated with fraud, such as repeated identity templates or coordinated submission networks. Natural language tools can provide multilingual assistance to applicants and citizens, translating instructions, answering routine questions and drafting communications, thereby reducing backlogs that in some embassies stretch into months or years of waiting time.

Yet the same triage logic that accelerates routine cases can quietly determine who receives additional scrutiny, and applicants often have limited visibility into why their case was routed to a slower track or how an automated tool shaped the summary a reviewing officer ultimately sees. A model trained on historical adjudication data can inherit the biases embedded in that data, potentially channelling applicants from particular regions, profiles or document types toward more intensive examination. This is not a hypothetical concern: the European Union’s AI Act has classified certain AI systems used in visa processing, migration and border control as high-risk, legally requiring safeguards including effective human oversight, risk management procedures, data governance standards and mechanisms for meaningful review of automated outputs.

Kilburg and Blaser are emphatic that in such cases the human signature at the end of a process is not enough if the person making the decision has neither the time nor the freedom to question what the technology has placed in front of them. They warn that when officers defer too heavily to algorithmic outputs, they risk becoming passive executors rather than active adjudicators, a shift that erodes both the legal discretion entrusted to them and the accountability that democratic systems expect of public officials. Automation bias, the well-documented human tendency to over-trust machine recommendations, becomes especially dangerous in consular contexts because a refused visa or flagged passport can separate families, block employment, or deny protection to someone fleeing danger.

Drawing on his background in psychology and AI adoption, Kilburg highlights an even subtler danger: that AI becomes the default frame through which a case is seen, narrowing the range of facts an officer considers before the officer has formed an independent judgment. Blaser, whose thirty-four-year Foreign Service career included senior consular leadership, puts the point in terms of accountability, noting that AI can help an officer see more, find something faster or spot a pattern that might otherwise be missed, but information is not judgment, and when a decision can change someone’s life, responsibility must remain with a human being. Their prescription is not a moratorium but a division of labour: machines should structure, accelerate and expand the information available, while humans retain the authority and responsibility to adjudicate.

The authors see the genuine benefit of AI as temporal rather than transformative. If automation absorbs repetitive document checks, routine queries and preliminary sorting, consular officials can reinvest their attention in the difficult cases that require judgment, empathy, context and genuine human attention, including refugee claims, complex fraud investigations and citizen emergencies abroad. Done well, they argue, AI will make services faster, borders more secure and officers more effective, but its greatest achievement will be quieter: giving consular teams the time and clarity to be more present, more empathetic and more humane in their dealings with the public.

That conclusion reframes the debate about AI at borders in a way that resonates far beyond consular affairs. The future of algorithmic governance in high-stakes public services, the authors suggest, is not fundamentally about technology at all; it is about people, and about designing systems in which the machine amplifies human oversight rather than quietly replacing it. For the millions of applicants whose futures are decided in consular windows every year, the difference between a tool that informs a decision and a system that effectively makes it may be the most important line in modern migration policy, and one that governments, regulators and citizens are only beginning to draw.

Subject of Research: The impact of artificial intelligence on consular affairs, visa processing and border control decisions

Article Title: How AI is impacting who gets to cross borders

Article References: How AI is impacting who gets to cross borders. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: artificial intelligence, consular affairs, visa processing, border security, immigration policy, human oversight, AI triage, passport systems, fraud detection, EU AI Act, algorithmic bias, automation bias