.png){kind=icon}
.png){kind=icon}
Return
Fair Risk Algorithms
.png){kind=icon}
PRE
AI
DOI：10.1146/annurev-statistics-033021-120649.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Machine learning algorithms are becoming ubiquitous in modern life. When used to help inform human decision making, they have been criticized by some for insufficient accuracy, an absence of transparency, and unfairness. Many of these concerns can be legitimate, although they are less convincing when compared with the uneven quality of human decisions. There is now a large literature in statistics and computer science offering a range of proposed improvements. In this article, we focus on machine learning algorithms used to forecast risk, such as those employed by judges to anticipate a convicted offender's future dangerousness and by physicians to help formulate a medical prognosis or ration scarce medical care. We review a variety of conceptual, technical, and practical features common to risk algorithms and offer suggestions for how their development and use might be meaningfully advanced. Fairness concerns are emphasized.
Keywords:
fairness
discrimination
algorithms
risk assessment
criminal justice
machine learning
optimal transport
conformation prediction
Journal
IF:
8.7
Papers:
211
Citations:
2.4K