Concern 34 · Working draft · editorial review in progress
Adversarial mislabeling
Adversarial mislabeling is the deliberate assignment of wrong training labels to influence what a model learns.
Understanding the concern
A label might identify an image, rate a message, or mark an application as suitable. If someone can alter these answers, the model may learn the attacker's judgments while the underlying examples remain unchanged.
This differs from accidental label noise and from clean-label poisoning, where labels remain plausible. Review needs to examine who can change labels, how changes are recorded, and whether suspicious disagreements receive independent attention.
An example
A dishonest marker changes selected answers in the practice key, so students learn a rule the teacher never intended.
Illustrative unless identified as a documented case.
Words in plain English
- Model
- The learned part of an AI system. It uses patterns found during training to produce an answer or prediction.
- Training
- The process of using examples to shape how an AI model behaves. Learning patterns from material does not establish that the material is true or fair.
- Data poisoning
- Deliberately changing training material to push a system toward unwanted behavior.
- Adversarial
- In this context, designed to challenge, trick, or attack a system.
Evidence and its limits
The attack taxonomy covers manipulation of training data and labels; the classroom example is illustrative.
NIST · 2025 · Technical report
Adversarial Machine Learning: A Taxonomy and Terminology of Attacks and Mitigations ↗ (opens in a new tab)Defines attack objectives and mechanisms across AI lifecycle stages. Taxonomy categories are not estimates of incident frequency.
Broader frameworks and background
These resources provide context. They do not establish every claim in this concern.
A question to discuss
Can important labels be changed without an independent check or a traceable record?
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.
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?
Justice
Justice means fair treatment, fair procedures, and attention to who receives benefits and burdens. It asks whether people with less power can be heard and seek repair.
Look at outcomes across different groups and individual cases. Provide a usable way to challenge decisions and correct harm. Equal wording in a policy does not guarantee equal treatment in practice.
Ask together: Who benefits, who bears the cost, and who can challenge the decision?
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
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