Fair Active Learning: Solving the Labeling Problem in Insurance

• URL: http://arxiv.org/abs/2112.09466v4
• Date: Mon, 20 May 2024 15:46:48 GMT
• Title: Fair Active Learning: Solving the Labeling Problem in Insurance
• Authors: Romuald Elie, Caroline Hillairet, François Hu, Marc Juillard,
• Abstract summary: The paper explores various active learning sampling methodologies and evaluates their impact on both synthetic and real insurance datasets. The proposed approach samples informative and fair instances, achieving a good balance between model predictive performance and fairness.
• Score: 2.5470832667329213
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper addresses significant obstacles that arise from the widespread use of machine learning models in the insurance industry, with a specific focus on promoting fairness. The initial challenge lies in effectively leveraging unlabeled data in insurance while reducing the labeling effort and emphasizing data relevance through active learning techniques. The paper explores various active learning sampling methodologies and evaluates their impact on both synthetic and real insurance datasets. This analysis highlights the difficulty of achieving fair model inferences, as machine learning models may replicate biases and discrimination found in the underlying data. To tackle these interconnected challenges, the paper introduces an innovative fair active learning method. The proposed approach samples informative and fair instances, achieving a good balance between model predictive performance and fairness, as confirmed by numerical experiments on insurance datasets.

Related papers

• Contrastive Learning of Asset Embeddings from Financial Time Series [8.595725772518332]
  We propose a novel contrastive learning framework to generate asset embeddings from financial time series data. Our approach leverages the similarity of asset returns over many subwindows to generate informative positive and negative samples. Experiments on real-world datasets demonstrate the effectiveness of the learned asset embeddings on benchmark industry classification and portfolio optimization tasks.
  arXiv  Detail & Related papers  (2024-07-26T10:26:44Z)
• Fair Active Learning in Low-Data Regimes [22.349886628823125]
  In machine learning applications, ensuring fairness is essential to avoid perpetuating social inequities. In this work, we address the challenges of reducing bias and improving accuracy in data-scarce environments. We introduce an innovative active learning framework that combines an exploration procedure inspired by posterior sampling with a fair classification subroutine. We demonstrate that this framework performs effectively in very data-scarce regimes, maximizing accuracy while satisfying fairness constraints with high probability.
  arXiv  Detail & Related papers  (2023-12-13T23:14:55Z)
• The Fairness Stitch: Unveiling the Potential of Model Stitching in Neural Network De-Biasing [0.043512163406552]
  This study introduces a novel method called "The Fairness Stitch" to enhance fairness in deep learning models. We conduct a comprehensive evaluation of two well-known datasets, CelebA and UTKFace. Our findings reveal a notable improvement in achieving a balanced trade-off between fairness and performance.
  arXiv  Detail & Related papers  (2023-11-06T21:14:37Z)
• Fair Few-shot Learning with Auxiliary Sets [53.30014767684218]
  In many machine learning (ML) tasks, only very few labeled data samples can be collected, which can lead to inferior fairness performance. In this paper, we define the fairness-aware learning task with limited training samples as the emphfair few-shot learning problem. We devise a novel framework that accumulates fairness-aware knowledge across different meta-training tasks and then generalizes the learned knowledge to meta-test tasks.
  arXiv  Detail & Related papers  (2023-08-28T06:31:37Z)
• Active Learning with Combinatorial Coverage [0.0]
  Active learning is a practical field of machine learning that automates the process of selecting which data to label. Current methods are effective in reducing the burden of data labeling but are heavily model-reliant. This has led to the inability of sampled data to be transferred to new models as well as issues with sampling bias. We propose active learning methods utilizing coverage to overcome these issues.
  arXiv  Detail & Related papers  (2023-02-28T13:43:23Z)
• 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)
• FairIF: Boosting Fairness in Deep Learning via Influence Functions with Validation Set Sensitive Attributes [51.02407217197623]
  We propose a two-stage training algorithm named FAIRIF. It minimizes the loss over the reweighted data set where the sample weights are computed. We show that FAIRIF yields models with better fairness-utility trade-offs against various types of bias.
  arXiv  Detail & Related papers  (2022-01-15T05:14:48Z)
• 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)
• Learning from Similarity-Confidence Data [94.94650350944377]
  We investigate a novel weakly supervised learning problem of learning from similarity-confidence (Sconf) data. We propose an unbiased estimator of the classification risk that can be calculated from only Sconf data and show that the estimation error bound achieves the optimal convergence rate.
  arXiv  Detail & Related papers  (2021-02-13T07:31:16Z)
• Fairness in Semi-supervised Learning: Unlabeled Data Help to Reduce Discrimination [53.3082498402884]
  A growing specter in the rise of machine learning is whether the decisions made by machine learning models are fair. We present a framework of fair semi-supervised learning in the pre-processing phase, including pseudo labeling to predict labels for unlabeled data. A theoretical decomposition analysis of bias, variance and noise highlights the different sources of discrimination and the impact they have on fairness in semi-supervised learning.
  arXiv  Detail & Related papers  (2020-09-25T05:48:56Z)
• Toward Optimal Probabilistic Active Learning Using a Bayesian Approach [4.380488084997317]
  Active learning aims at reducing the labeling costs by an efficient and effective allocation of costly labeling resources. By reformulating existing selection strategies within our proposed model, we can explain which aspects are not covered in current state-of-the-art.
  arXiv  Detail & Related papers  (2020-06-02T15:59:42Z)

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

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.