Related papers: Causal Feature Selection for Algorithmic Fairness

Causal Feature Selection for Algorithmic Fairness

URL: http://arxiv.org/abs/2006.06053v2
Date: Thu, 31 Mar 2022 08:09:21 GMT
Title: Causal Feature Selection for Algorithmic Fairness
Authors: Sainyam Galhotra, Karthikeyan Shanmugam, Prasanna Sattigeri and Kush R. Varshney
Abstract summary: We consider fairness in the integration component of data management. We propose an approach to identify a sub-collection of features that ensure the fairness of the dataset.
Score: 61.767399505764736
License: http://creativecommons.org/licenses/by/4.0/
Abstract: The use of machine learning (ML) in high-stakes societal decisions has encouraged the consideration of fairness throughout the ML lifecycle. Although data integration is one of the primary steps to generate high quality training data, most of the fairness literature ignores this stage. In this work, we consider fairness in the integration component of data management, aiming to identify features that improve prediction without adding any bias to the dataset. We work under the causal interventional fairness paradigm. Without requiring the underlying structural causal model a priori, we propose an approach to identify a sub-collection of features that ensure the fairness of the dataset by performing conditional independence tests between different subsets of features. We use group testing to improve the complexity of the approach. We theoretically prove the correctness of the proposed algorithm to identify features that ensure interventional fairness and show that sub-linear conditional independence tests are sufficient to identify these variables. A detailed empirical evaluation is performed on real-world datasets to demonstrate the efficacy and efficiency of our technique.

Related papers

Uncertainty Aware Learning for Language Model Alignment [97.36361196793929]
We propose uncertainty-aware learning (UAL) to improve the model alignment of different task scenarios. We implement UAL in a simple fashion -- adaptively setting the label smoothing value of training according to the uncertainty of individual samples. Experiments on widely used benchmarks demonstrate that our UAL significantly and consistently outperforms standard supervised fine-tuning.
arXiv Detail & Related papers (2024-06-07T11:37:45Z)
Balancing Fairness and Accuracy in Data-Restricted Binary Classification [14.439413517433891]
This paper proposes a framework that models the trade-off between accuracy and fairness under four practical scenarios. Experiments on three datasets demonstrate the utility of the proposed framework as a tool for quantifying the trade-offs.
arXiv Detail & Related papers (2024-03-12T15:01:27Z)
Fairness Without Harm: An Influence-Guided Active Sampling Approach [32.173195437797766]
We aim to train models that mitigate group fairness disparity without causing harm to model accuracy. The current data acquisition methods, such as fair active learning approaches, typically require annotating sensitive attributes. We propose a tractable active data sampling algorithm that does not rely on training group annotations.
arXiv Detail & Related papers (2024-02-20T07:57:38Z)
An Operational Perspective to Fairness Interventions: Where and How to Intervene [9.833760837977222]
We present a holistic framework for evaluating and contextualizing fairness interventions. We demonstrate our framework with a case study on predictive parity. We find predictive parity is difficult to achieve without using group data.
arXiv Detail & Related papers (2023-02-03T07:04:33Z)
Fair and Optimal Classification via Post-Processing [10.163721748735801]
This paper provides a complete characterization of the inherent tradeoff of demographic parity on classification problems. We show that the minimum error rate achievable by randomized and attribute-aware fair classifiers is given by the optimal value of a Wasserstein-barycenter problem.
arXiv Detail & Related papers (2022-11-03T00:04:04Z)
HyperImpute: Generalized Iterative Imputation with Automatic Model Selection [77.86861638371926]
We propose a generalized iterative imputation framework for adaptively and automatically configuring column-wise models. We provide a concrete implementation with out-of-the-box learners, simulators, and interfaces.
arXiv Detail & Related papers (2022-06-15T19:10:35Z)
On Modality Bias Recognition and Reduction [70.69194431713825]
We study the modality bias problem in the context of multi-modal classification. We propose a plug-and-play loss function method, whereby the feature space for each label is adaptively learned. Our method yields remarkable performance improvements compared with the baselines.
arXiv Detail & Related papers (2022-02-25T13:47:09Z)
Information Theoretic Measures for Fairness-aware Feature Selection [27.06618125828978]
We develop a framework for fairness-aware feature selection, based on information theoretic measures for the accuracy and discriminatory impacts of features. Specifically, our goal is to design a fairness utility score for each feature which quantifies how this feature influences accurate as well as nondiscriminatory decisions.
arXiv Detail & Related papers (2021-06-01T20:11:54Z)
Can Active Learning Preemptively Mitigate Fairness Issues? [66.84854430781097]
dataset bias is one of the prevailing causes of unfairness in machine learning. We study whether models trained with uncertainty-based ALs are fairer in their decisions with respect to a protected class. We also explore the interaction of algorithmic fairness methods such as gradient reversal (GRAD) and BALD.
arXiv Detail & Related papers (2021-04-14T14:20:22Z)
Decomposed Adversarial Learned Inference [118.27187231452852]
We propose a novel approach, Decomposed Adversarial Learned Inference (DALI) DALI explicitly matches prior and conditional distributions in both data and code spaces. We validate the effectiveness of DALI on the MNIST, CIFAR-10, and CelebA datasets.
arXiv Detail & Related papers (2020-04-21T20:00:35Z)

This list is automatically generated from the titles and abstracts of the papers in this site.