Concern 84 · Working draft · editorial review in progress
Attribution & credit for annotation work
Annotation attribution concerns whether the people who label and evaluate training material receive appropriate recognition for their contribution.
Understanding the concern
A model may be described mainly through its designers and computing resources, although human reviewers supplied judgments that shaped its behavior. Recognition can include documentation of roles and expertise as well as individual credit where people want it.
Credit alone does not replace fair pay, and naming workers without permission can expose them to unwanted attention. The principle is to acknowledge contribution in a way that respects workers' choices and accurately describes the work.
An example
A project may list its software authors while leaving the people who interpreted difficult examples invisible.
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.
- Annotation
- Human work that labels or describes examples for an AI system. Labels can involve judgment and can be wrong.
Evidence and its limits
Dataset documentation supports identifying contributors and processes; labor assessment supplies context for meaningful recognition and protections.
Gebru and colleagues · Communications of the ACM · Research proposal
Datasheets for Datasets ↗ (opens in a new tab)Documentation questions cover composition, collection, time, uses, permissions, and maintenance. This is a documentation method, not proof of a particular dataset's defects.
Fairwork · Workplace assessment
Fairwork Appen Report 2025 ↗ (opens in a new tab)Assesses one Philippine operation using worker and management evidence, including improvements during assessment. Not an industry-wide judgment.
Broader frameworks and background
These resources provide context. They do not establish every claim in this concern.
A question to discuss
How can contributors receive meaningful recognition without losing privacy or being substituted credit for fair compensation?
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
These are editorial judgments from the register, not measured probabilities or predictions about your situation.
About the register and scoring →More ways to explore
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