Learning Adversarially Fair and Transferable Representations
Authors: David Madras, Elliot Creager, Toniann Pitassi, Richard Zemel
ICML 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We validate experimentally that classifiers trained naively (without fairness constraints) from representations learned by our algorithm achieve their respective fairness desiderata; furthermore, we show empirically that these representations achieve fair transfer they admit fair predictors on unseen tasks, even when those predictors are not explicitly specified to be fair. |
| Researcher Affiliation | Academia | 1Department of Computer Science, University of Toronto, Toronto, Canada 2Vector Institute, Canada. |
| Pseudocode | Yes | ALGORITHM 1 Evaluation scheme for fair classification (Y = Y ) & transfer learning (Y = Y ). |
| Open Source Code | No | The paper does not contain any explicit statement about providing open-source code for the methodology or a link to a code repository. |
| Open Datasets | Yes | We evaluate the performance of our model on fair classification on the UCI Adult dataset5, We aimed to predict each person s income category (either greater or less than 50K/year). |
| Dataset Splits | Yes | We split our test set ( 20, 000 examples) into transfer-train, -validation, and -test sets. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used for running the experiments, such as GPU or CPU models. |
| Software Dependencies | No | The paper mentions optimization algorithms like SGD and Adam, but does not provide specific version numbers for any software dependencies or libraries used in the implementation. |
| Experiment Setup | Yes | Each sub-figure shows the accuracy-fairness trade-off (for varying values of γ; we set α = 1, β = 0 for all classification experiments); We used L1 loss for the classifier; For transfer learning, we set our reconstruction coefficient β = 1; We can optionally set our classification coefficient α to 0; trained LAFTR (α = 0, β = 1, ℓ2 loss for the decoder) on the full training set. |