D-CALM: A Dynamic Clustering-based Active Learning Approach for Mitigating Bias

• URL: http://arxiv.org/abs/2305.17013v1
• Date: Fri, 26 May 2023 15:17:43 GMT
• Title: D-CALM: A Dynamic Clustering-based Active Learning Approach for Mitigating Bias
• Authors: Sabit Hassan and Malihe Alikhani
• Abstract summary: In this paper, we propose a novel adaptive clustering-based active learning algorithm, D-CALM, that dynamically adjusts clustering and annotation efforts. Experiments on eight datasets for a diverse set of text classification tasks, including emotion, hatespeech, dialog act, and book type detection, demonstrate that our proposed algorithm significantly outperforms baseline AL approaches.
• Score: 13.008323851750442
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Despite recent advancements, NLP models continue to be vulnerable to bias. This bias often originates from the uneven distribution of real-world data and can propagate through the annotation process. Escalated integration of these models in our lives calls for methods to mitigate bias without overbearing annotation costs. While active learning (AL) has shown promise in training models with a small amount of annotated data, AL's reliance on the model's behavior for selective sampling can lead to an accumulation of unwanted bias rather than bias mitigation. However, infusing clustering with AL can overcome the bias issue of both AL and traditional annotation methods while exploiting AL's annotation efficiency. In this paper, we propose a novel adaptive clustering-based active learning algorithm, D-CALM, that dynamically adjusts clustering and annotation efforts in response to an estimated classifier error-rate. Experiments on eight datasets for a diverse set of text classification tasks, including emotion, hatespeech, dialog act, and book type detection, demonstrate that our proposed algorithm significantly outperforms baseline AL approaches with both pretrained transformers and traditional Support Vector Machines. D-CALM showcases robustness against different measures of information gain and, as evident from our analysis of label and error distribution, can significantly reduce unwanted model bias.

Related papers

• Explanatory Interactive Machine Learning for Bias Mitigation in Visual Gender Classification [6.296044623811203]
  Explanatory interactive learning (XIL) enables users to guide model training in machine learning (ML) by providing feedback on the model's explanations.<n>We investigate two state-of-the-art XIL strategies, i.e., CAIPI and Right for the Right Reasons (Bounded), as well as a novel hybrid approach that combines both strategies.<n> Experimental results demonstrate the effectiveness of these methods in guiding ML models to focus on relevant image features.
  arXiv  Detail & Related papers  (2026-02-07T13:41:42Z)
• Let Samples Speak: Mitigating Spurious Correlation by Exploiting the Clusterness of Samples [11.727747752958436]
  Deep learning models often learn features that spuriously correlate with the class label during training but are irrelevant to the prediction task.<n>Existing methods typically address this issue by annotating potential spurious attributes, or filtering spurious features based on some empirical assumptions.<n>We propose a data-oriented approach to mitigate the spurious correlation in deep learning models.
  arXiv  Detail & Related papers  (2025-12-28T10:54:51Z)
• Reference-Specific Unlearning Metrics Can Hide the Truth: A Reality Check [60.77691669644931]
  We propose Functional Alignment for Distributional Equivalence (FADE), a novel metric that measures distributional similarity between unlearned and reference models.<n>We show that FADE captures functional alignment across the entire output distribution, providing a principled assessment of genuine unlearning.<n>These findings expose fundamental gaps in current evaluation practices and demonstrate that FADE provides a more robust foundation for developing and assessing truly effective unlearning methods.
  arXiv  Detail & Related papers  (2025-10-14T20:50:30Z)
• Diffusing DeBias: Synthetic Bias Amplification for Model Debiasing [18.864168501187393]
  We introduce Diffusing DeBias (DDB) as a plug-in for common methods of unsupervised model debiasing.<n>Specifically, our approach adopts conditional diffusion models to generate synthetic bias-aligned images.<n>By tackling the fundamental issue of bias-conflicting training samples in learning auxiliary models, our proposed method beats current state-of-the-art in multiple benchmark datasets.
  arXiv  Detail & Related papers  (2025-02-13T18:17:03Z)
• DISCO: DISCovering Overfittings as Causal Rules for Text Classification Models [6.369258625916601]
  Post-hoc interpretability methods fail to capture the models' decision-making process fully. Our paper introduces DISCO, a novel method for discovering global, rule-based explanations. DISCO supports interactive explanations, enabling human inspectors to distinguish spurious causes in the rule-based output.
  arXiv  Detail & Related papers  (2024-11-07T12:12:44Z)
• MeLIAD: Interpretable Few-Shot Anomaly Detection with Metric Learning and Entropy-based Scoring [2.394081903745099]
  We propose MeLIAD, a novel methodology for interpretable anomaly detection. MeLIAD is based on metric learning and achieves interpretability by design without relying on any prior distribution assumptions of true anomalies. Experiments on five public benchmark datasets, including quantitative and qualitative evaluation of interpretability, demonstrate that MeLIAD achieves improved anomaly detection and localization performance.
  arXiv  Detail & Related papers  (2024-09-20T16:01:43Z)
• DIVE: Subgraph Disagreement for Graph Out-of-Distribution Generalization [44.291382840373]
  This paper addresses the challenge of out-of-distribution generalization in graph machine learning. Traditional graph learning algorithms falter in real-world scenarios where this assumption fails. A principal factor contributing to this suboptimal performance is the inherent simplicity bias of neural networks.
  arXiv  Detail & Related papers  (2024-08-08T12:08:55Z)
• DECIDER: Leveraging Foundation Model Priors for Improved Model Failure Detection and Explanation [18.77296551727931]
  We propose DECIDER, a novel approach that leverages priors from large language models (LLMs) and vision-language models (VLMs) to detect failures in image models. DECIDER consistently achieves state-of-the-art failure detection performance, significantly outperforming baselines in terms of the overall Matthews correlation coefficient.
  arXiv  Detail & Related papers  (2024-08-01T07:08:11Z)
• Noisy Correspondence Learning with Self-Reinforcing Errors Mitigation [63.180725016463974]
  Cross-modal retrieval relies on well-matched large-scale datasets that are laborious in practice. We introduce a novel noisy correspondence learning framework, namely textbfSelf-textbfReinforcing textbfErrors textbfMitigation (SREM)
  arXiv  Detail & Related papers  (2023-12-27T09:03:43Z)
• XAL: EXplainable Active Learning Makes Classifiers Better Low-resource Learners [71.8257151788923]
  We propose a novel Explainable Active Learning framework (XAL) for low-resource text classification. XAL encourages classifiers to justify their inferences and delve into unlabeled data for which they cannot provide reasonable explanations. Experiments on six datasets show that XAL achieves consistent improvement over 9 strong baselines.
  arXiv  Detail & Related papers  (2023-10-09T08:07:04Z)
• Taming Small-sample Bias in Low-budget Active Learning [20.900107811622803]
  Firth bias reduction can provably reduce the bias during the model training process but might hinder learning if its coefficient is not adaptive to the learning progress. We propose curriculum Firth bias reduction (CHAIN) that can automatically adjust the coefficient to be adaptive to the training process.
  arXiv  Detail & Related papers  (2023-06-19T16:42:11Z)
• Feature-Level Debiased Natural Language Understanding [86.8751772146264]
  Existing natural language understanding (NLU) models often rely on dataset biases to achieve high performance on specific datasets. We propose debiasing contrastive learning (DCT) to mitigate biased latent features and neglect the dynamic nature of bias. DCT outperforms state-of-the-art baselines on out-of-distribution datasets while maintaining in-distribution performance.
  arXiv  Detail & Related papers  (2022-12-11T06:16:14Z)
• Prototypical Classifier for Robust Class-Imbalanced Learning [64.96088324684683]
  We propose textitPrototypical, which does not require fitting additional parameters given the embedding network. Prototypical produces balanced and comparable predictions for all classes even though the training set is class-imbalanced. We test our method on CIFAR-10LT, CIFAR-100LT and Webvision datasets, observing that Prototypical obtains substaintial improvements compared with state of the arts.
  arXiv  Detail & Related papers  (2021-10-22T01:55:01Z)
• Learning Bias-Invariant Representation by Cross-Sample Mutual Information Minimization [77.8735802150511]
  We propose a cross-sample adversarial debiasing (CSAD) method to remove the bias information misused by the target task. The correlation measurement plays a critical role in adversarial debiasing and is conducted by a cross-sample neural mutual information estimator. We conduct thorough experiments on publicly available datasets to validate the advantages of the proposed method over state-of-the-art approaches.
  arXiv  Detail & Related papers  (2021-08-11T21:17:02Z)
• Task-agnostic Continual Learning with Hybrid Probabilistic Models [75.01205414507243]
  We propose HCL, a Hybrid generative-discriminative approach to Continual Learning for classification. The flow is used to learn the data distribution, perform classification, identify task changes, and avoid forgetting. We demonstrate the strong performance of HCL on a range of continual learning benchmarks such as split-MNIST, split-CIFAR, and SVHN-MNIST.
  arXiv  Detail & Related papers  (2021-06-24T05:19:26Z)

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.