Concern 203 · Working draft · editorial review in progress
Undisclosed AI in workflows
Undisclosed AI in workflows means people do not know that an AI system contributes to a decision or service affecting them.
Understanding the concern
AI may operate behind the scenes by ranking applications, flagging cases, or drafting recommendations. A human may deliver the final result without making that earlier role visible. Affected people can then struggle to ask the right questions or correct an error.
This differs from not knowing that a chatbot is artificial. Here the interaction may be entirely with a person while AI influences the process. Disclosure should explain what the system contributed and who remains responsible, rather than treating any human involvement as proof of meaningful review.
An example
A job applicant receives a decision from a recruiter without knowing that an automated ranking determined which applications were examined.
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
ICO guidance addresses assisted decisions; NYC provides a jurisdiction-specific notice example for covered employment tools. Coverage is not universal.
Information Commissioner's Office and Alan Turing Institute · Official guidance
Explaining decisions made with AI ↗ (opens in a new tab)Explains audience-sensitive accounts of AI-assisted decisions; legal provisions must be checked against current jurisdiction and context.
New York City Department of Consumer and Worker Protection · Official regulatory guidance
Automated Employment Decision Tools: Frequently Asked Questions ↗ (opens in a new tab)Explains Local Law 144's covered tools, bias audits and notices; it does not certify fairness in every employment use.
Broader frameworks and background
These resources provide context. They do not establish every claim in this concern.
A question to discuss
Can affected people learn where AI influenced the process and how to challenge a mistake?
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.
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?
Accountability
Accountability means being answerable for decisions and taking responsibility for repair. It requires more than saying that the software made the choice.
Name the person or team responsible, keep useful records, and give them authority to correct problems. Make the route to a complaint clear to the people affected.
Ask together: Who will answer for the outcome and help put things right?
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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