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1 results

  • 6 - Data for Machine Learning

  • from Part II - Doing Human-Centered Machine Learning
  • Edited by Rebecca Fiebrink, University of the Arts London, Marco Gillies, Goldsmiths, University of London, Gonzalo Ramos, CNRS, Sorbonne Université
  • Book:
    Human-Centered Machine Learning
    Published online:
    11 September 2026
    Print publication:
    03 September 2026, pp 123-150

  • Summary

    In the past decade, human computation and crowdsourcing research has shown that, by tapping into human intelligence over the Web, one can collect massive amounts of labeled data for machine learning. At the same time, research on AI ethics has highlighted the “bias-in, bias-out” problem in machine learning and the importance of paying attention to the nature of the data on which models are trained. In this chapter, we shine a spotlight on the humans who help generate data, the various psychological processes – biases, motivation, and identity – that can affect the data generation process, and how such influences can be amplified or reduced through thoughtful design of crowdsourcing processes and work practices.