DalšíDalší průvodce
AI Fairness Metrics: Demographic Parity to Equalized Odds
Technický
PRŮVODCE společností
Counterfactual Fairness
Counterfactual fairness is a causal fairness criterion: for the same individual and background circumstances, a prediction should remain the same in a modeled counterfactual world where the protected attribute differs.
Na této stránce3 min čtení
Přehled
The criterion can expose indirect pathways that simple group-parity checks miss, but it depends on a causal model and judgments about which pathways should count as acceptable.
Hluboký ponor
Kusner, Loftus, Russell and Silva introduced counterfactual fairness in a 2017 NeurIPS paper using structural causal models. Informally, a predictor is counterfactually fair for an individual if its prediction in the observed world is the same as its prediction in a counterfactual world in which that individual belonged to a different protected group, given the same background circumstances. The method asks more than whether two groups have equal average outcomes: it asks whether changing the protected attribute in a causal model would change the particular person’s prediction. A structural causal model represents variables and the causal relationships used to generate them. The analyst intervenes on the protected attribute and evaluates the prediction in the resulting counterfactual world. The paper demonstrates the idea in a law-school success prediction example. This does not establish that counterfactual fairness is the uniquely correct ethical or legal standard; it formalizes one fairness intuition under specified causal assumptions. The assumptions carry normative weight. The analyst must decide which variables are causes, which observed features are descendants of protected status, which causal pathways are impermissible, and what “same person in a different group” means. In practice, the relevant counterfactual may be unobservable and the causal graph may be uncertain. Measurement error, hidden confounding and historical discrimination can undermine estimates. A system can satisfy a chosen counterfactual criterion while still producing group-level disparities or harmful outcomes under another fairness definition. Conversely, a predictor can use a protected attribute in a model to correct a harmful proxy path, depending on the causal policy chosen. Reports should publish the model, assumptions, permitted and prohibited pathways, uncertainty and comparisons to other measures rather than presenting one score as proof of fairness.
Strategický dopad
Riziko a bezpečnost
Katastrofické a každodenní škody AI závisí na tom, kdo rozumí rizikům a kdo může jednat.
Jasnější rozhodnutí
Veřejná a odborná gramotnost určuje, zda je silná bezpečnostní politika politicky možná.
Prorážením humbuku
Jasná vysvětlení snižují zachytávání humbukem, PR v laboratoři a vágní etické divadlo.
The Future of Counterfactual Fairness
Research continues to develop causal fairness methods and tools for uncertain or disputed causal graphs. Teams should treat counterfactual conclusions as conditional on model assumptions, retain sensitivity analyses, and use them with other technical and legal assessments rather than as a single universal measure. Keep a dated record of the primary source or study behind each claim and revisit conclusions when new evidence or implementation details emerge. Open questions include how to validate causal pathways when records are incomplete and communities disagree about acceptable pathways.
Real-World Implementace
A lender models whether a credit decision would change if an applicant’s race differed while the model’s causal assumptions held other relevant background factors fixed.
A hiring team tests whether a score changes under a gender counterfactual rather than relying only on overall selection rates.
A researcher documents which descendants of a protected attribute are considered legitimate inputs before computing counterfactual predictions.
A regulator compares counterfactual results with disparate-impact, calibration and error-rate measures because no single criterion fully defines fairness.
Rizika a zábradlí
Zacházení s existenčním rizikem jako sci-fi, zatímco schopnosti kombinují.
Matoucí bezpečnost povrchových produktů se zarovnáním pod vysokou autonomií.
Neanglické a neodborné publikum ponechává pouze nekvalitní zdroje.
Plán implementace
Oddělte rizika poškození produktu, nesprávného použití a ztráty kontroly/nesouladu.
Zeptejte se, jaké důkazy by změnily váš pohled na časové osy a závažnost.
Upřednostňujte primární zdroje a konkrétní hodnocení před marketingovými tvrzeními.
Identifikujte jednu akční cestu: kariéru, politiku, financování nebo dovednosti – nejen povědomí.
Pokračujte v objevování
Free newsletter
Get the daily AI briefing
Three verified AI stories every weekday morning, written in plain English. Free forever, no ads.
One email each weekday. Unsubscribe in one click. We never sell or share your address.
Test yourself
Take the Counterfactual Fairness quiz
Instant feedback on every answer, and a shareable certificate with a verifiable ID once you pass a course.
Support free AI education. AI Understanding is a 501(c)(3) nonprofit — no ads, no paywall, ever. Make a donation
Často kladené otázky
What is Counterfactual Fairness?
Counterfactual fairness is a causal fairness criterion: for the same individual and background circumstances, a prediction should remain the same in a modeled counterfactual world where the protected attribute differs. The criterion can expose indirect pathways that simple group-parity checks miss, but it depends on a causal model and judgments about which pathways should count as acceptable.
What comparison is central to counterfactual fairness?
The criterion compares one individual’s prediction with a counterfactual prediction under a different protected-attribute value.
Which modeling tool does the original counterfactual-fairness paper use?
The paper develops the criterion using tools from causal inference, including structural causal models.
What happens to the protected attribute in the counterfactual test?
In the structural causal model, the analyst sets the protected attribute to an alternative value and evaluates that same individual’s prediction in the counterfactual world.
Why can counterfactual fairness detect issues that group averages may miss?
The metric is individualized and causal, rather than only comparing aggregate group rates.
Which assumption most limits interpretation of a counterfactual-fairness result?
The method depends on the structural causal model and analyst decisions about causal relationships.
Učte se dál
Související průvodci
Pro toto téma bylo vybráno více průvodců