Detecting Bias in the Presence of Spatial Autocorrelation

Subhabrata Majumdar, Cheryl Flynn, Ritwik Mitra
Proceedings of The Algorithmic Fairness through the Lens of Causality and Robustness, PMLR 171:6-18, 2022.

Abstract

In spite of considerable practical importance, current algorithmic fairness literature lacks technical methods to account for underlying geographic dependency while evaluating or mitigating bias issues for spatial data. We initiate the study of bias in spatial applications in this paper, taking the first step towards formalizing this line of quantitative methods. Bias in spatial data applications often gets confounded by underlying spatial autocorrelation. We propose hypothesis testing methodology to detect the presence and strength of this effect, then account for it by using a spatial filtering-based approach—in order to enable application of existing bias detection metrics. We evaluate our proposed methodology through numerical experiments on real and synthetic datasets, demonstrating that in the presence of several types of confounding effects due to the underlying spatial structure our testing methods perform well in maintaining low type-II errors and nominal type-I errors.

Cite this Paper

BibTeX
@InProceedings{pmlr-v171-majumdar22a, title = {Detecting Bias in the Presence of Spatial Autocorrelation}, author = {Majumdar, Subhabrata and Flynn, Cheryl and Mitra, Ritwik}, booktitle = {Proceedings of The Algorithmic Fairness through the Lens of Causality and Robustness}, pages = {6--18}, year = {2022}, editor = {Schrouff, Jessica and Dieng, Awa and Rateike, Miriam and Kwegyir-Aggrey, Kweku and Farnadi, Golnoosh}, volume = {171}, series = {Proceedings of Machine Learning Research}, month = {13 Dec}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v171/majumdar22a/majumdar22a.pdf}, url = {https://proceedings.mlr.press/v171/majumdar22a.html}, abstract = {In spite of considerable practical importance, current algorithmic fairness literature lacks technical methods to account for underlying geographic dependency while evaluating or mitigating bias issues for spatial data. We initiate the study of bias in spatial applications in this paper, taking the first step towards formalizing this line of quantitative methods. Bias in spatial data applications often gets confounded by underlying spatial autocorrelation. We propose hypothesis testing methodology to detect the presence and strength of this effect, then account for it by using a spatial filtering-based approach—in order to enable application of existing bias detection metrics. We evaluate our proposed methodology through numerical experiments on real and synthetic datasets, demonstrating that in the presence of several types of confounding effects due to the underlying spatial structure our testing methods perform well in maintaining low type-II errors and nominal type-I errors.} }
Endnote
%0 Conference Paper %T Detecting Bias in the Presence of Spatial Autocorrelation %A Subhabrata Majumdar %A Cheryl Flynn %A Ritwik Mitra %B Proceedings of The Algorithmic Fairness through the Lens of Causality and Robustness %C Proceedings of Machine Learning Research %D 2022 %E Jessica Schrouff %E Awa Dieng %E Miriam Rateike %E Kweku Kwegyir-Aggrey %E Golnoosh Farnadi %F pmlr-v171-majumdar22a %I PMLR %P 6--18 %U https://proceedings.mlr.press/v171/majumdar22a.html %V 171 %X In spite of considerable practical importance, current algorithmic fairness literature lacks technical methods to account for underlying geographic dependency while evaluating or mitigating bias issues for spatial data. We initiate the study of bias in spatial applications in this paper, taking the first step towards formalizing this line of quantitative methods. Bias in spatial data applications often gets confounded by underlying spatial autocorrelation. We propose hypothesis testing methodology to detect the presence and strength of this effect, then account for it by using a spatial filtering-based approach—in order to enable application of existing bias detection metrics. We evaluate our proposed methodology through numerical experiments on real and synthetic datasets, demonstrating that in the presence of several types of confounding effects due to the underlying spatial structure our testing methods perform well in maintaining low type-II errors and nominal type-I errors.
APA
Majumdar, S., Flynn, C. & Mitra, R.. (2022). Detecting Bias in the Presence of Spatial Autocorrelation. Proceedings of The Algorithmic Fairness through the Lens of Causality and Robustness, in Proceedings of Machine Learning Research 171:6-18 Available from https://proceedings.mlr.press/v171/majumdar22a.html.

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