Concern 382 · Working draft · editorial review in progress
Demographic parity vs equalized odds
Fairness-metric conflict occurs when different reasonable ways of measuring fairness give different answers about the same system.
Understanding the concern
Demographic parity asks whether groups receive a favourable result at similar rates. Equalized odds asks whether the system makes similar kinds of mistakes across groups, considering the recorded outcome. These questions are different. Equal approval rates, for example, do not automatically mean equal error rates.
Think of judging a school by equal pass rates versus equally accurate marking. Either measure can reveal something useful, but neither alone explains the whole situation. Data quality and unequal opportunities also matter. Choosing a metric is partly a decision about which harms to prevent, so affected people should understand what the chosen test leaves out.
An example
A lending tool passes a check on approval rates but still wrongly rejects qualified applicants more often in one group.
Illustrative unless identified as a documented case.
Evidence and its limits
The papers define statistical criteria and their limits. Recorded labels are not assumed to be perfect measures of qualification or future behaviour.
Hardt, Price and Srebro · Research paper
Equality of Opportunity in Supervised Learning ↗ (opens in a new tab)Formal group-fairness criteria, adjustments and limits of statistical tests.
Alexandra Chouldechova · Research paper
Fair Prediction with Disparate Impact ↗ (opens in a new tab)Trade-offs among risk-score calibration and error-rate criteria; measured outcomes and assumptions matter.
Broader frameworks and background
These resources provide context. They do not establish every claim in this concern.
A question to discuss
Which kind of unfairness are we trying to reduce, and what does this metric fail to show?
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.
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?
Dignity
Dignity means recognizing each person's worth regardless of usefulness, status, or a system's score. People deserve respect and a voice in matters that affect them.
Explain important decisions, avoid humiliating treatment, and keep room for a person's story. Do not treat a prediction as a complete account of who someone is.
Ask together: Does this use treat each person as someone whose experience matters?
Listening
Listening means trying to understand someone's experience before preparing an answer. People who use a service, work with it, or are judged by it may notice problems its designers miss.
Invite people to describe what happened in their own words. Repeat back what you understood and let them correct it. A quiet person or a complaint that is hard to measure may still reveal something important.
Ask together: Whose experience have we not heard yet?
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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