Add to ORKGFrameworks for fair machine learning are envisioned to play an important practical role in the evaluation, training, and selection of machine learning models. In particular, fairness metrics are meant to provide responsible agents with actionable standards for evaluating ML models and conditions which those models should achieve. However, recent studies suggest that fair ML frameworks and metrics do not provide sufficient and actionable guidance for agents. This short paper outlines the main content of a working paper wherein I draw lessons from philosophical debates concerning action-guidance to build a conceptual account that can be applied to analyze whether and when fair ML frameworks and metrics can generate determinate evaluations of ...
There is growing concern that decision-making informed by machine learning (ML) algorithms may unfai...
Over the last years, a wide spread of Machine Learning in increasingly more, especially sensitive ar...
There is growing concern that decision-making informed by machine learning (ML) algorithms may unfai...
Fairness-aware machine learning (fair-ml) techniques are algorithmic interventions designed to ensur...
With the growing prevalence of AI algorithms and their use to prepare and even execute decisions, th...
The problem of fair machine learning has drawn much attention over the last few years and the bulk o...
Fairness emerged as an important requirement to guarantee that Machine Learning (ML) predictive syst...
Machine Learning has become more and more prominent in our daily lives as the Information Age and Fo...
One of the difficulties of artificial intelligence is to ensure that model decisions are fair and fr...
International audienceFairness emerged as an important requirement to guarantee that Machine Learnin...
Recent years have seen the development of many open-source ML fairness toolkits aimed at helping ML ...
International audienceFairness emerged as an important requirement to guarantee that Machine Learnin...
International audienceFairness emerged as an important requirement to guarantee that Machine Learnin...
There is growing concern that decision-making informed by machine learning (ML) algorithms may unfai...
Abstract Recent advances in machine learning methods have created opportunities to el...
There is growing concern that decision-making informed by machine learning (ML) algorithms may unfai...
Over the last years, a wide spread of Machine Learning in increasingly more, especially sensitive ar...
There is growing concern that decision-making informed by machine learning (ML) algorithms may unfai...
Fairness-aware machine learning (fair-ml) techniques are algorithmic interventions designed to ensur...
With the growing prevalence of AI algorithms and their use to prepare and even execute decisions, th...
The problem of fair machine learning has drawn much attention over the last few years and the bulk o...
Fairness emerged as an important requirement to guarantee that Machine Learning (ML) predictive syst...
Machine Learning has become more and more prominent in our daily lives as the Information Age and Fo...
One of the difficulties of artificial intelligence is to ensure that model decisions are fair and fr...
International audienceFairness emerged as an important requirement to guarantee that Machine Learnin...
Recent years have seen the development of many open-source ML fairness toolkits aimed at helping ML ...
International audienceFairness emerged as an important requirement to guarantee that Machine Learnin...
International audienceFairness emerged as an important requirement to guarantee that Machine Learnin...
There is growing concern that decision-making informed by machine learning (ML) algorithms may unfai...
Abstract Recent advances in machine learning methods have created opportunities to el...
There is growing concern that decision-making informed by machine learning (ML) algorithms may unfai...
Over the last years, a wide spread of Machine Learning in increasingly more, especially sensitive ar...
There is growing concern that decision-making informed by machine learning (ML) algorithms may unfai...