Concern 22 · Working draft · editorial review in progress
Proxy variables encoding protected attributes
A proxy variable is information that stands in for another characteristic, sometimes carrying sensitive group patterns into a decision.
Understanding the concern
Removing a field labeled race or gender does not necessarily remove related information. A location, school, or spending pattern may still be associated with protected characteristics. Whether its use is unfair depends on the task and effects.
The concern is about indirect influence, not a claim that every correlated feature must be banned. Reviewers should test the pathway from the feature to the decision and ask whether it has a defensible purpose.
An example
A housing tool may use a neighborhood score that partly reflects a history of unequal access to resources.
Illustrative unless identified as a documented case.
Words in plain English
- Proxy
- Something used as a stand-in for what we really want to measure. A postal code, for example, may indirectly reveal other information about a person.
Evidence and its limits
The lifecycle framework supports examining measurement choices and the social meaning of chosen variables.
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.
Broader frameworks and background
These resources provide context. They do not establish every claim in this concern.
A question to discuss
What does this feature really measure, and could it carry an unfair pattern by another name?
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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