On the fairness of disentangled representations
Recently there has been a significant interest in learning disentangled representations, as they promise increased interpretability, generalization to unseen scenarios and faster learning on downstream tasks. In this paper, we investigate the usefulness of different notions of disentanglement for im...
| Main Authors: | , , , , , |
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| Format: | Conference item |
| Language: | English |
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NeurIPS
2019
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| _version_ | 1826258271250415616 |
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| author | Locatello, F Abbati, G Rainforth, T Bauer, S Schölkopf, B Bachem, O |
| author_facet | Locatello, F Abbati, G Rainforth, T Bauer, S Schölkopf, B Bachem, O |
| author_sort | Locatello, F |
| collection | OXFORD |
| description | Recently there has been a significant interest in learning disentangled representations, as they promise increased interpretability, generalization to unseen scenarios and faster learning on downstream tasks. In this paper, we investigate the usefulness of different notions of disentanglement for improving the fairness of downstream prediction tasks based on representations. We consider the setting where the goal is to predict a target variable based on the learned representation of high-dimensional observations (such as images) that depend on both the target variable and an unobserved sensitive variable. We show that in this setting both the optimal and empirical predictions can be unfair, even if the target variable and the sensitive variable are independent. Analyzing the representations of more than 12 600 trained state-of-the-art disentangled models, we observe that several disentanglement scores are consistently correlated with increased fairness, suggesting that disentanglement may be a useful property to encourage fairness when sensitive variables are not observed. |
| first_indexed | 2024-03-06T18:31:19Z |
| format | Conference item |
| id | oxford-uuid:09bfd495-4deb-4f13-a615-dc6326614212 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-06T18:31:19Z |
| publishDate | 2019 |
| publisher | NeurIPS |
| record_format | dspace |
| spelling | oxford-uuid:09bfd495-4deb-4f13-a615-dc63266142122022-03-26T09:20:02ZOn the fairness of disentangled representationsConference itemhttp://purl.org/coar/resource_type/c_5794uuid:09bfd495-4deb-4f13-a615-dc6326614212EnglishSymplectic ElementsNeurIPS2019Locatello, FAbbati, GRainforth, TBauer, SSchölkopf, BBachem, ORecently there has been a significant interest in learning disentangled representations, as they promise increased interpretability, generalization to unseen scenarios and faster learning on downstream tasks. In this paper, we investigate the usefulness of different notions of disentanglement for improving the fairness of downstream prediction tasks based on representations. We consider the setting where the goal is to predict a target variable based on the learned representation of high-dimensional observations (such as images) that depend on both the target variable and an unobserved sensitive variable. We show that in this setting both the optimal and empirical predictions can be unfair, even if the target variable and the sensitive variable are independent. Analyzing the representations of more than 12 600 trained state-of-the-art disentangled models, we observe that several disentanglement scores are consistently correlated with increased fairness, suggesting that disentanglement may be a useful property to encourage fairness when sensitive variables are not observed. |
| spellingShingle | Locatello, F Abbati, G Rainforth, T Bauer, S Schölkopf, B Bachem, O On the fairness of disentangled representations |
| title | On the fairness of disentangled representations |
| title_full | On the fairness of disentangled representations |
| title_fullStr | On the fairness of disentangled representations |
| title_full_unstemmed | On the fairness of disentangled representations |
| title_short | On the fairness of disentangled representations |
| title_sort | on the fairness of disentangled representations |
| work_keys_str_mv | AT locatellof onthefairnessofdisentangledrepresentations AT abbatig onthefairnessofdisentangledrepresentations AT rainfortht onthefairnessofdisentangledrepresentations AT bauers onthefairnessofdisentangledrepresentations AT scholkopfb onthefairnessofdisentangledrepresentations AT bachemo onthefairnessofdisentangledrepresentations |