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Found 7 papers, 1 papers with code
Fairness-aware machine learning: a perspective
We need to analyze machine learning process as a whole to systematically explain the roots of discrimination occurrence, which will allow to devise global machine learning optimization criteria for guaranteed prevention, as opposed to pushing empirical constraints into existing algorithms case-by-case.
A note on adjusting $R^2$ for using with cross-validation
We show how to adjust the coefficient of determination ($R^2$) when used for measuring predictive accuracy via leave-one-out cross-validation.
A survey on measuring indirect discrimination in machine learning
In this survey we review and organize various discrimination measures that have been used for measuring discrimination in data, as well as in evaluating performance of discrimination-aware predictive models.
Computers and Society Applications
Predicting respiratory motion for real-time tumour tracking in radiotherapy
Accurate predictions of lung tumor motion are expected to improve the precision of radiation treatment by controlling the position of a couch or a beam in order to compensate for respiratory motion during radiation treatment.
Optimal estimates for short horizon travel time prediction in urban areas
One approach is to predict travel times for route segments, and sum those estimates to obtain a prediction for the whole route.
On the relation between accuracy and fairness in binary classification
We argue that comparison of non-discriminatory classifiers needs to account for different rates of positive predictions, otherwise conclusions about performance may be misleading, because accuracy and discrimination of naive baselines on the same dataset vary with different rates of positive predictions.
Predictive User Modeling with Actionable Attributes
We study how to learn to choose the value of an actionable attribute in order to maximize the probability of a desired outcome in predictive modeling.
