arXiv (Cornell University)· 2017· Preprint
Counterfactual Fairness
- 879citations
- 2017year
Short summary
A new framework uses causal inference to define and measure counterfactual fairness, ensuring decisions remain the same even if an individual's demographic group changes.
AI-generated from the title and abstract; the full text is not read.
Key points
- Develops a framework for counterfactual fairness using causal inference tools.
- Defines fairness as a decision being the same in the actual world and a counterfactual world with a different demographic group.
- Demonstrates the framework on predicting law school success to combat discriminatory practices.
AI-generated from the title and abstract; the full text is not read.
Abstract
Machine learning can impact people with legal or ethical consequences when it is used to automate decisions in areas such as insurance, lending, hiring, and predictive policing. In many of these scenarios, previous decisions have been made that are unfairly biased against certain subpopulations, for example those of a particular race, gender, or sexual orientation. Since this past data may be biased, machine learning predictors must account for this to avoid perpetuating or creating discriminatory practices. In this paper, we develop a framework for modeling fairness using tools from causal inference. Our definition of counterfactual fairness captures the intuition that a decision is fair towards an individual if it is the same in (a) the actual world and (b) a counterfactual world where the individual belonged to a different demographic group. We demonstrate our framework on a real-world problem of fair prediction of success in law school.
The authors' abstract, as published at the source. arXiv (Cornell University), 2017 · DOI ↗
Continue with a free account
Ask the paper: 3 free questions a day about this paper; save it, get its citation, new summaries every day for your field. Takeaways are Premium.
Continue free on the webSign in with Google or Apple; no card needed. You come back to this paper.
On your phone:
Field: Safety Research
Safety ResearchSocial Sciences