Metric-Fair Classifier Derandomization
Authors: Jimmy Wu, Yatong Chen, Yang Liu
ICML 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We study the problem of classifier derandomization in machine learning: given a stochastic binary classifier f : X [0, 1], sample a deterministic classifier ˆf : X {0, 1} that approximates the output of f in aggregate over any data distribution. Recent work revealed how to efficiently derandomize a stochastic classifier with strong output approximation guarantees, but at the cost of individual fairness that is, if f treated similar inputs similarly, ˆf did not. In this paper, we initiate a systematic study of classifier derandomization with metric fairness guarantees. We show that the prior derandomization approach is almost maximally metric-unfair, and that a simple random threshold derandomization achieves optimal fairness preservation but with weaker output approximation. We then devise a derandomization procedure that provides an appealing tradeoff between these two: if f is α-metric fair according to a metric d with a locality-sensitive hash (LSH) family, then our derandomized ˆf is, with high probability, O(α)-metric fair and a close approximation of f. We also prove generic results applicable to all (fair and unfair) classifier derandomization procedures, including a bias-variance decomposition and reductions between various notions of metric fairness. |
| Researcher Affiliation | Academia | Department of Computer Science and Engineering, University of California, Santa Cruz, USA. Correspondence to: Jimmy Wu <jimmywu126@gmail.com>, Yatong Chen <ychen592@ucsc.edu>, Yang Liu (corresponding author) <yangliu@ucsc.edu>. |
| Pseudocode | No | The paper describes procedures and definitions mathematically (e.g., equations for FRT and FLS), but it does not include any explicitly labeled "Pseudocode" or "Algorithm" blocks. |
| Open Source Code | No | The paper does not contain any statement about making its source code available, nor does it provide any links to a code repository. |
| Open Datasets | No | The paper is theoretical and does not perform experiments with datasets, thus it does not mention specific datasets or their public availability for training. |
| Dataset Splits | No | The paper is theoretical and does not conduct experiments, therefore it does not provide details on training, validation, or test dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not describe experiments, therefore it does not mention specific hardware used for running experiments. |
| Software Dependencies | No | The paper is theoretical and focuses on mathematical derivations and algorithm design, not specific software implementations, thus it does not list software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe experiments, therefore it does not provide details about experimental setup, hyperparameters, or training configurations. |