Concern 407 · Working draft · editorial review in progress
Cross-agency data fusion
Cross-agency data fusion combines records held by different organisations to create a more complete picture of people or events.
Understanding the concern
Each organisation may hold only part of the story: an address, a service record or a transaction. Linking them can reveal relationships that no single dataset shows. AI may assist matching and analysis, but errors can also spread when two people are wrongly treated as one or an old record is taken as current.
Think of joining several partial maps. The combined map can be useful, yet it may expose a route no contributor expected to reveal. Sharing should have a clear purpose, access limits and a way to trace conclusions back to records. More connected data is not automatically more accurate or appropriate to use.
An example
An outdated address in one agency's file affects a conclusion drawn by another after records are linked.
Illustrative unless identified as a documented case.
Words in plain English
- AI
- Software that learns patterns from examples or uses rules to perform tasks such as recognizing images or generating text. It can make mistakes and does not understand every situation as a person would.
Evidence and its limits
The sources explain aggregation and linkage risks. The example is hypothetical and not evidence of one particular interagency system.
Office of the Director of National Intelligence · Historical policy framework
Intelligence Community Policy Framework for Commercially Available Information ↗ (opens in a new tab)Privacy and civil-liberties issues in aggregated commercial data; not disclosure of classified operational capabilities.
Garfinkel, Near, Dajani, Singer and Guttman; NIST · Technical guidance
De-Identifying Government Datasets: Techniques and Governance, NIST SP 800-188 ↗ (opens in a new tab)Disclosure risks, release controls and limits of removing identifiers.
Broader frameworks and background
These resources provide context. They do not establish every claim in this concern.
- OECD AI Principles (opens in a new tab)
framework
- NIST AI RMF (opens in a new tab)
framework
A question to discuss
Which new inferences become possible, and can people correct an error across every system that received it?
Keep track of different views, what needs checking, and any next step in your own discussion notes.
Open discussion notes →Principles for the conversation
Before you finish · reflect together
Virtues and principles to consider
Virtues are qualities we practice in how we treat people and make choices. They are not scores for judging someone. Use these ideas to explore what a thoughtful response could look like.
Dignity
Dignity means recognizing each person's worth regardless of usefulness, status, or a system's score. People deserve respect and a voice in matters that affect them.
Explain important decisions, avoid humiliating treatment, and keep room for a person's story. Do not treat a prediction as a complete account of who someone is.
Ask together: Does this use treat each person as someone whose experience matters?
Trustworthiness
Trustworthiness means giving people good reasons to rely on you. It grows when promises, actions, and evidence line up. With AI, a friendly voice or a famous brand is not enough: people need to know what was checked and what could still go wrong.
In practice, keep promises small enough to test, record mistakes, and tell people when the tool's limits affect them. If a service promises human review, make sure a person really has time and authority to review it.
Ask together: What would give the people affected a sound reason to trust this use?
Stewardship
Stewardship means caring for resources and systems that others depend on, including people who will use them later. It looks beyond the benefit of a quick launch.
Plan for maintenance, energy use, data protection, and eventual shutdown. Ask who will look after the tool when the original team moves on and how future users can avoid inheriting hidden costs.
Ask together: What are we responsible for caring for over time?
Editorial ratings
These are editorial judgments from the register, not measured probabilities or predictions about your situation.
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