Concern 80 · Working draft · editorial review in progress
Inconsistent label quality
Inconsistent labels occur when similar examples receive different answers, which can confuse a model or reveal uncertainty in the task itself.
Understanding the concern
Some differences are mistakes caused by unclear instructions or rushed work. Others reflect genuine disagreement: two careful readers may reasonably interpret a joke or a political comment differently. Treating every disagreement as bad work can erase useful information.
The aim should be to understand why labels differ before forcing one answer. A dataset may need clearer instructions, expert review, or a record of multiple interpretations. A majority vote is a decision rule, not automatic proof of truth.
An example
Two reviewers disagree on whether a remark is sarcastic because they imagine different contexts.
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.
Evidence and its limits
Plank distinguishes genuine human variation from noise and discusses its implications for data, modeling, and evaluation.
Barbara Plank · EMNLP 2022 · Research position paper
The Problem of Human Label Variation: On Ground Truth in Data, Modeling and Evaluation ↗ (opens in a new tab)Explains why human disagreement can contain useful information rather than merely label noise.
Broader frameworks and background
These resources provide context. They do not establish every claim in this concern.
A question to discuss
Is this disagreement an error to correct or a meaningful difference the system should preserve?
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.
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