Concern 2 · Working draft · editorial review in progress
Selection bias
Selection bias occurs when the way people or examples enter a dataset makes the dataset a poor guide to the people it will be used for.
Understanding the concern
The problem starts at the collection gate. People who volunteer, own a certain device, or use a particular service may differ from those who do not. A large dataset can still inherit that selective entry process.
This differs from simply having too few examples. Collecting more through the same narrow channel may strengthen the distortion. Researchers should compare who can enter the dataset with who will later depend on the system.
An example
A health survey collected only through a fitness app may miss people who cannot afford a smartphone or do not exercise.
Illustrative unless identified as a documented case.
Evidence and its limits
The lifecycle framework describes representation problems; datasheets ask how examples were collected and selected.
Suresh and Guttag · EAAMO 2021 · Research framework
A Framework for Understanding Sources of Harm throughout the Machine Learning Life Cycle ↗ (opens in a new tab)Framework distinguishes historical, representation, measurement, aggregation, learning, evaluation, and deployment bias. These categories can overlap.
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.
Broader frameworks and background
These resources provide context. They do not establish every claim in this concern.
A question to discuss
Who could never have entered the dataset in the first place?
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
Search for more writing
For more specific or recent material, open a search below or ask for a web summary. AI-generated summaries can contain mistakes; open the original articles to check them.