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Resilience of Supervised Learning Algorithms to Discriminatory Poisoning of Training Data

EasyChair Preprint no. 6274

2 pagesDate: August 11, 2021

Abstract

We address the concern that training datasets for supervised learning could have been poisoned through discrimination by i) defining and modeling discrimination as a dataset poisoning, ii) proposing novel interventional mixtures to inhibit discrimination, iii) and evaluating these and other methods addressing discrimination on synthetic and real-world datasets.

Keyphrases: Data Poisoning, decision systems, discrimination, fairness, machine learning

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
@Booklet{EasyChair:6274,
  author = {Przemyslaw Grabowicz and Nicholas Perello},
  title = {Resilience of Supervised Learning Algorithms to Discriminatory Poisoning of Training Data},
  howpublished = {EasyChair Preprint no. 6274},

  year = {EasyChair, 2021}}
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