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The predictive reframing of machine learning applications: good predictions and bad measurements
16 references·0 citing·57 second hop
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References

  • 2021A Survey on Bias and Fairness in Machine Learning5,798 cited
  • 2017Measuring the natural rate of interest: International trends and determinants700 cited
  • 2015Predicting poverty and wealth from mobile phone metadata700 cited
  • 2017A Philosophy for the Science of Well-Being485 cited
  • 2021Measurement and Fairness362 cited
  • 2019Understanding from Machine Learning Models229 cited
  • 2016ChaLearn LAP 2016: First Round Challenge on First Impressions - Dataset and Results215 cited
  • 2017Combining disparate data sources for improved poverty prediction and mapping193 cited
  • 2018Economic Policy for Artificial Intelligence170 cited
  • 2009Models, measurement and computer simulation: the changing face of experimentation162 cited
  • 2021Data models, representation and adequacy-for-purpose118 cited
  • 2016Is Construct Validation Valid?99 cited
  • 1987VALIDITY90 cited
  • 2017Calibration: Modelling the measurement process79 cited
  • 2020Unemployment Rate Forecasting: A Hybrid Approach69 cited
  • 2015Science Outside the Laboratory

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