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1Scan for outdated or missing drivers - takes under a minute2Clear out junk files and repair common Windows errors3Fix the driver behind crashes, sound loss and screen glitchesResponsible AI in migration services means using AI in ways that protect migrants’ human rights, privacy and dignity, with transparency, accountability, human oversight and active efforts to address bias. The practical test is not whether a system is new or efficient, but what service or decision it affects, whose data and interests are involved, and what happens when it is wrong.
Where AI may be used in migration services
Migration services include services for people who migrate and the institutions that support or govern migration. They are not the same as IT services that help a business move its systems to the cloud. The International Organization for Migration (IOM) describes possible uses that range from giving people information to supporting government processes. These examples identify areas of application; they do not establish how widely AI is deployed or how well particular systems work.
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| Use | What it might do | Why the consequences matter |
|---|---|---|
| Multilingual information and guidance | Answer questions or help people find relevant information in their languages. IOM’s 2026–2028 AI strategy discusses migrant-facing digital tools and information in people’s languages. | An inaccurate or unclear answer could mislead someone trying to understand a service or next step. Users need a way to check important information and reach a person. |
| Government digitization and data analysis | Support digital government processes or analyze data used in migration policy. IOM discusses AI for governments and the wider migration ecosystem. | The stakes depend on whether a tool only organizes information or could influence a referral, access to a service, eligibility, or another consequential outcome. |
| Biometric identity management | Use biometric information alongside AI in identity-related systems. IOM’s 2026 publication examines normative and legal frameworks for biometrics and AI in identity management. | Identity systems involve sensitive information and can have serious effects if a system’s output is wrong or used without adequate review. |
These categories should not be treated as interchangeable. An informational assistant, a triage tool and an identity-related system have different consequences and call for different levels of scrutiny.
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What makes an AI system responsible in this setting?
IOM’s migration analysis frames AI through existing international human-rights rules, standards and principles. Its 18 September 2024 governing-bodies document recommends that Member States establish and enforce ethical guidelines for AI and data analytics in migration processes, including transparency, accountability, human oversight, privacy protection and active work to eliminate bias.
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Start with the purpose and the people affected
Define the user need before choosing a tool. Ask whether AI is necessary to meet it, who may be affected, and whether people with limited language or digital access can use the service. Be especially clear about whether the system provides information, helps staff prioritize work, or may shape an outcome for an individual.
Minimize and protect personal data
Migration services may handle sensitive personal information. IOM’s Data Bulletin on data protection places privacy and protection at the centre of migration-data discussions and describes safeguards across collection, storage, use, disclosure and other processing. In practice, identify what the system collects or infers, why each item is needed, where it goes, who can access it, how long it is retained, and whether a vendor or partner can share it onward.
Make human oversight real
Transparency means people can understand when AI is involved and what role it plays. Oversight means more than having a staff member nominally in the loop: the service should identify who owns the decision, who can examine a disputed or erroneous output, and how a person can get help. The applicable legal rights and remedies depend on the jurisdiction; do not assume a universal appeal route.
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Assess for bias and human-rights risks
Before deployment, assess the potential effects on migrants and refugees, including risks of discrimination, privacy harm and erosion of human rights. Revisit the assessment when the system, data, purpose or operating context changes, and use feedback and incident reports to correct problems. A one-time approval is not a substitute for continuing monitoring and remediation.
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Questions to ask before choosing or deploying a system
- Purpose: What concrete need does the system address, and is AI necessary for it?
- People: Who could be affected, including people facing language, disability or digital-access barriers?
- Consequences: Could the output influence identity, access, referral, eligibility or another consequential outcome?
- Data: What personal information is collected, inferred, stored, shared or sent to outside parties? Is each use necessary and protected?
- Communication: Can people understand when AI is being used and what it does?
- Review: Can a person get an understandable explanation or meaningful human review when something goes wrong?
- Ownership: Which organization and named role are accountable for the system and for correcting harm?
- Partners: If a vendor or partner processes information, what controls apply to access, retention, security and onward sharing?
- Monitoring: How will the organization assess bias and human-rights risks before launch and revisit them as the service changes?
These questions are a practical decision aid, not a universal compliance checklist or a substitute for jurisdiction-specific law.
How to compare service designs
When deciding between an AI-assisted service and another design, compare the options against the same criteria. A simpler non-AI process may be preferable if it meets the need with less data or fewer risks; conversely, an AI component is not responsible merely because it improves speed or convenience.
| Criterion | What to examine |
|---|---|
| Purpose and consequences | What the tool is meant to do and how its output could affect people. |
| Data handling | Sensitivity, collection, retention, access and sharing, including with vendors. |
| Transparency and access | Whether people can understand the system’s role and reach an accessible human channel. |
| Risk assessment | How bias and human-rights risks are assessed before use and monitored afterward. |
| Accountability and review | Who owns the system, who can review errors and how harms are addressed. |
IOM’s materials support these comparison dimensions, but do not provide a validated universal scoring scale. Assigning numerical weights or ranking vendors without evidence would create false precision.
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What the available evidence does—and does not—show
IOM’s publications describe relevant applications, principles and risks, but the cited materials do not establish a current AI adoption rate, comparative accuracy figure or measured outcome across migration services. Efficiency claims alone therefore cannot demonstrate fairness, accuracy or benefit. Any claim about a particular system should be judged on evidence for that system, its users and the decision it supports.
Relevant IOM publications include Artificial intelligence, migration and mobility: implications for policy and practice, Chapter 11 of World Migration Report 2022 (publication record: 2021); the 2026–2028 AI strategy, “Leading AI for migration. Responsibly. At scale”; the 18 September 2024 governing-bodies document on technological change; Data Bulletin: Informing a Global Compact for Migration – Data Protection, Issue 18 (January 2019); and the 2026 publication record for Biometrics and Artificial Intelligence in Identity Management: an assessment of normative and legal frameworks.
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