📚 node [[counterfactual_fairness|counterfactual fairness]]
Welcome! Nobody has contributed anything to 'counterfactual_fairness|counterfactual fairness' yet. You can:
  • Write something in the document below!
    • There is at least one public document in every node in the Agora. Whatever you write in it will be integrated and made available for the next visitor to read and edit.
  • Write to the Agora from social media.
    • If you follow Agora bot on a supported platform and include the wikilink [[counterfactual_fairness|counterfactual fairness]] in a post, the Agora will link it here and optionally integrate your writing.
  • Sign up as a full Agora user.
    • As a full user you will be able to contribute your personal notes and resources directly to this knowledge commons. Some setup required :)
⥅ related node [[counterfactual_fairness]]
⥅ node [[counterfactual_fairness]] pulled by Agora

counterfactual fairness

Go back to the [[AI Glossary]]

#fairness A fairness metric that checks whether a classifier produces the same result for one individual as it does for another individual who is identical to the first, except with respect to one or more sensitive attributes. Evaluating a classifier for counterfactual fairness is one method for surfacing potential sources of bias in a model.

See "When Worlds Collide: Integrating Different Counterfactual Assumptions in Fairness" for a more detailed discussion of counterfactual fairness.

📖 stoas
⥱ context