Concern 18 · Working draft · editorial review in progress
Institutional bias
Institutional bias means that an AI may favor perspectives from institutions that publish, digitize, or distribute more material.
Understanding the concern
Universities, large media organizations, and public agencies can leave extensive searchable records. Smaller organizations and informal communities may have valuable knowledge that appears less often or is harder to collect.
Publication volume is not the same as truth or representativeness. The original claim that elite institutions produce most of every corpus needs case-specific evidence. Good documentation should name actual sources rather than assume who dominates.
An example
A history summary may rely on an institution's official archive while missing residents' accounts of the same event.
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.
- Corpus
- A collection of material, often text, used to train or study a system. Corpora is the plural.
Evidence and its limits
The corpus study shows why source inspection matters; neither source proves the original universal claim about elite institutions.
Dodge and colleagues · EMNLP 2021 · Dataset audit
Documenting Large Webtext Corpora: A Case Study on C4 ↗ (opens in a new tab)Examines sources and filters in C4, including disproportionate removal of minority-associated material. Findings concern this corpus and process.
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
Which institutions supplied the evidence, and whose accounts remain outside it?
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.