machine learningdiscriminationfairnesspredictionMachine learning algorithms have become increasingly common and have affect many aspects of our life. However, because the objective of most of the standard, ofShimao, HajimeKomiyama, JunpeiKhern-am-nuai, Warut...
machine learningbiasMachine Learning algorithms are becoming widely deployed in real world decision-making. Ensuring fairness in algorithmic decision-making is a crucialdoi:10.2139/ssrn.3408275Fu, RunshanAseri, ManmohanSingh, Param VirSrinivasan, Kannan...
Implementation and evaluation of provably Rawlsian fair ML algorithms for contextual bandits. Related Work/Citations: Rawlsian Fairness for Machine Learning (https://arxiv.org/abs/1610.09559) Unbiased Offline Evaluation of Contextual-bandit-based News Article Recommendation Algorithms (https://arxiv.org/...
The widespread implementation of machine learning in safety-critical domains has raised ethical concerns regarding algorithmic discrimination. In such sett
Are machine learning (ML) algorithms biased against minorities and women?A 2016 Pro Publica article investigated COMPAS, an ML algorithm designed to predict recidivism by those convicted of crimes. The article found the tool to be racially biased, of major concern since it was being used by ...
As such honest and decent precautions and analysis are needed to ensure algorithms are equal and reasonable without discrimination. Moreover, for antibiotic decision making further ethical considerations need to be taken into account including the effect on other individuals outside of the patient being...
machine learning to treat this kind of problemsFootnote1. Machine learning methods are actually far from being fair, just, or equitable in any way. After all, standard pattern analysis is often about model fitting and not the gender issue. Undoubtedly, attaining fair machine learning algorithms ...
Such techniques are called “reductions” because they reduce the problem we wish to solve into a different problem—typically a more standard problem for which many algorithms already exist. Our fair learning reduction is, in fact, a special case of a more general reduction for imposing constrain...
As machine learning infiltrates society, scientists are trying to help ward off injustice. By Rachel Courtland Twitter Facebook Email In 2015, a worried father asked Rhema Vaithianathan a question that still weighs on her mind. A small crowd had gathered in a basement room in Pittsburgh, ...
In contrast to many existing works that critically rely on the discreteness of sensitive attributes and response variables, the proposed penalty is able to handle versatile formats of the sensitive attributes, so it is more extensively applicable in practice than many existing algorithms. This penalty...