Prediction with Model-Based Neutrality

  • Kazuto Fukuchi
  • Jun Sakuma
  • Toshihiro Kamishima
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8189)


With recent developments in machine learning technology, the resulting predictions can now have a significant impact on the lives and activities of individuals. In some cases, predictions made by machine learning can result unexpectedly in unfair treatments to individuals. For example, if the results are highly dependent on personal attributes, such as gender or ethnicity, hiring decisions might be deemed discriminatory. This paper investigates the neutralization of a probabilistic model with respect to another probabilistic model, referred to as a viewpoint. We present a novel definition of neutrality for probabilistic models, η-neutrality, and introduce a systematic method that uses the maximum likelihood estimation to enforce the neutrality of a prediction model. Our method can be applied to various machine learning algorithms, as demonstrated by η-neutral logistic regression and η-neutral linear regression.


neutrality fairness discrimination logistic regression linear regression classification regression social responsibility 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Kazuto Fukuchi
    • 1
  • Jun Sakuma
    • 1
  • Toshihiro Kamishima
    • 2
  1. 1.University of TsukubaTsukubaJapan
  2. 2.AIST Tsukuba Central 2National Institute of Advanced Industrial Science and Technology (AIST)TsukubaJapan

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