Concern 748 · Working draft · editorial review in progress
Investigative reporting workflows
Investigative-reporting workflow risk arises when AI helps search large records but also introduces missed evidence, false leads or shortcuts that weaken an investigation.
Understanding the concern
A reporter may use AI to find patterns in thousands of documents, transcribe interviews or identify records worth reading. This can make an investigation possible. But a model may miss an unusual item or invent a connection, and its summary can shape the reporter's attention before the underlying evidence is checked.
Surveys show journalists using AI across both routine and substantive tasks. They do not establish that every use improves reporting. Think of a research assistant pointing to boxes in an archive: the pointers are leads, not verified findings. Journalists still need to inspect important records, corroborate claims and protect people affected by the story.
An example
A document-analysis tool highlights a suspicious pattern, but the reporter must determine whether it reflects misconduct or a harmless recording convention.
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.
- Model
- The learned part of an AI system. It uses patterns found during training to produce an answer or prediction.
Evidence and its limits
The survey records reported uses and attitudes. The example illustrates verification needs, not a measured accuracy rate or a documented investigation.
Reuters Institute for the Study of Journalism 2025 · Original survey
AI adoption by UK journalists and their newsrooms: surveying applications, approaches, and attitudes ↗ (opens in a new tab)1004 UK journalists surveyed in 2024; reported uses and attitudes. Does not establish causal effects on reporting quality.
Broader frameworks and background
These resources provide context. They do not establish every claim in this concern.
A question to discuss
Which AI-generated leads have been independently checked against original records and other sources?
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.
Learning together
Learning together means treating experience as something a group can examine and improve. It values honest feedback over appearing successful.
Agree on a question, try something manageable, and compare what different people observed. Share failures as well as gains. Decide together what the evidence supports and what remains uncertain.
Ask together: What have we learned together that should guide the next step?
Humility
Humility means recognizing the limits of our knowledge and being willing to learn from others. Expertise can be valuable without answering every question.
State where evidence is weak, invite criticism, and listen to people with different experience. Leave room to revise a decision when new information appears.
Ask together: What might we be missing, and who could help us see it?
Truthfulness
Truthfulness means making claims that match the evidence and being clear about what we do not know. It includes correcting mistakes rather than protecting a polished story.
Check sources, label guesses, and distinguish a trial result from a proven benefit. If AI helped produce a claim, someone still needs to take responsibility for checking it.
Ask together: What can we honestly say, and what still needs checking?
Editorial ratings
These are editorial judgments from the register, not measured probabilities or predictions about your situation.
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