Learning and evaluating classifiers under sample selection bias
Abstract
Classifier learning methods commonly assume that the training data consist of randomly drawn examples from the same distribution as the test examples about which the learned model is expected to make predictions. In many practical situations, however, this assumption is violated, in a problem known in econometrics as sample selection bias. In this paper, we formalize the sample selection bias problem in machine learning terms and study analytically and experimentally how a number of well-known classifier learning methods are affected by it. We also present a bias correction method that is particularly useful for classifier evaluation under sample selection bias.
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Prior work on detecting discrimination has focused on measuring correlation or mutual information between inputs and outputs, discrepancies in fractions of inputs that produce a given output, or discrepancies in output probability distributions.
“Prior work on detecting discrimination has focused on measuring correlation or mutual information between inputs and outputs [79], discrepancies in the fractions of inputs that produce a given output [18, 19, 39-42, 87-89, 91], or discrepancies in output probability distributions [51].”
From Fairness testing: testing software for discrimination (2017) · cites this paper as [88]
Reducing discrimination (CV score) in classifiers often fails to remove causal discrimination and discrimination against certain groups.
“Reducing discrimination (CV score) in classifiers [18, 40, 88, 91], as our evaluation has shown, often fails to remove causal discrimination and discrimination against certain groups.”
From Fairness testing: testing software for discrimination (2017) · cites this paper as [88]
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- Authors
- Zadrozny, B.
- Affiliations
- IBM (United States) (US)
- Corresponding author
- Zadrozny, Bianca
- ORCID
- Zadrozny, Bianca
- Venue
- Twenty-first international conference on Machine learning - ICML '04
- Publisher
- ACM Press
- Published
- 2004
- Pages
- 114
- OpenAlex
- W2032536435
- Cited by
- 800
- References
- 17
- Citations by year
- 2026: 13 · 2025: 19 · 2024: 25 · 2023: 42 · 2022: 55 · 2021: 76 · 2020: 109 · 2019: 49 · 2018: 63 · 2017: 35 · 2016: 37 · 2015: 45 · 2014: 33 · 2013: 29 · 2012: 31
- Type
- conference abstract
- Language
- en
- Open access
- closed
- Locations
- International Conference on Machine Learning (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Data Classification
- Physical Sciences › Computer Science › Artificial Intelligence › Imbalanced Data Classification Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Algorithms
- Keywords
- sample selection bias, classifier training, classifier evaluation, bias correction, training data distribution, econometrics
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