Bipol: A Novel Multi-Axes Bias Evaluation Metric with Explainability for NLP

• URL: http://arxiv.org/abs/2304.04029v2
• Date: Sat, 16 Sep 2023 15:47:24 GMT
• Title: Bipol: A Novel Multi-Axes Bias Evaluation Metric with Explainability for NLP
• Authors: Lama Alkhaled, Tosin Adewumi and Sana Sabah Sabry
• Abstract summary: We introduce bipol, a new metric with explainability, for estimating social bias in text data. In a step to address this challenge we create a novel metric that involves a two-step process. We create a large dataset for training models in bias detection and make it publicly available.
• Score: 0.276240219662896
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We introduce bipol, a new metric with explainability, for estimating social bias in text data. Harmful bias is prevalent in many online sources of data that are used for training machine learning (ML) models. In a step to address this challenge we create a novel metric that involves a two-step process: corpus-level evaluation based on model classification and sentence-level evaluation based on (sensitive) term frequency (TF). After creating new models to detect bias along multiple axes using SotA architectures, we evaluate two popular NLP datasets (COPA and SQUAD). As additional contribution, we created a large dataset (with almost 2 million labelled samples) for training models in bias detection and make it publicly available. We also make public our codes.

Related papers

• Numerical Literals in Link Prediction: A Critical Examination of Models and Datasets [2.5999037208435705]
  Link Prediction models that incorporate numerical literals have shown minor improvements on existing benchmark datasets. It is unclear whether a model is actually better in using numerical literals, or better capable of utilizing the graph structure. We propose a methodology to evaluate LP models that incorporate numerical literals.
  arXiv  Detail & Related papers  (2024-07-25T17:55:33Z)
• Downstream-Pretext Domain Knowledge Traceback for Active Learning [138.02530777915362]
  We propose a downstream-pretext domain knowledge traceback (DOKT) method that traces the data interactions of downstream knowledge and pre-training guidance. DOKT consists of a traceback diversity indicator and a domain-based uncertainty estimator. Experiments conducted on ten datasets show that our model outperforms other state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-07-20T01:34:13Z)
• IBADR: an Iterative Bias-Aware Dataset Refinement Framework for Debiasing NLU models [52.03761198830643]
  We propose IBADR, an Iterative Bias-Aware dataset Refinement framework. We first train a shallow model to quantify the bias degree of samples in the pool. Then, we pair each sample with a bias indicator representing its bias degree, and use these extended samples to train a sample generator. In this way, this generator can effectively learn the correspondence relationship between bias indicators and samples.
  arXiv  Detail & Related papers  (2023-11-01T04:50:38Z)
• Bipol: Multi-axes Evaluation of Bias with Explainability in Benchmark Datasets [1.7417978715317002]
  We investigate five English NLP benchmark datasets and two Swedish datasets for bias, along multiple axes. We use bipol, a novel multi-axes bias metric with explainability, to estimate and explain how much bias exists in these datasets.
  arXiv  Detail & Related papers  (2023-01-28T09:28:19Z)
• Quantifying Human Bias and Knowledge to guide ML models during Training [0.0]
  We introduce an experimental approach to dealing with skewed datasets by including humans in the training process. We ask humans to rank the importance of features of the dataset, and through rank aggregation, determine the initial weight bias for the model. We show that collective human bias can allow ML models to learn insights about the true population instead of the biased sample.
  arXiv  Detail & Related papers  (2022-11-19T20:49:07Z)
• The Word is Mightier than the Label: Learning without Pointillistic Labels using Data Programming [11.536162323162099]
  Most advanced supervised Machine Learning (ML) models rely on vast amounts of point-by-point labelled training examples. Hand-labelling vast amounts of data may be tedious, expensive, and error-prone.
  arXiv  Detail & Related papers  (2021-08-24T19:11:28Z)
• Combining Feature and Instance Attribution to Detect Artifacts [62.63504976810927]
  We propose methods to facilitate identification of training data artifacts. We show that this proposed training-feature attribution approach can be used to uncover artifacts in training data. We execute a small user study to evaluate whether these methods are useful to NLP researchers in practice.
  arXiv  Detail & Related papers  (2021-07-01T09:26:13Z)
• Few-Shot Named Entity Recognition: A Comprehensive Study [92.40991050806544]
  We investigate three schemes to improve the model generalization ability for few-shot settings. We perform empirical comparisons on 10 public NER datasets with various proportions of labeled data. We create new state-of-the-art results on both few-shot and training-free settings.
  arXiv  Detail & Related papers  (2020-12-29T23:43:16Z)
• BENN: Bias Estimation Using Deep Neural Network [37.70583323420925]
  We present BENN -- a novel bias estimation method that uses a pretrained unsupervised deep neural network. Given a ML model and data samples, BENN provides a bias estimation for every feature based on the model's predictions. We evaluated BENN using three benchmark datasets and one proprietary churn prediction model used by a European Telco.
  arXiv  Detail & Related papers  (2020-12-23T08:25:35Z)
• LOGAN: Local Group Bias Detection by Clustering [86.38331353310114]
  We argue that evaluating bias at the corpus level is not enough for understanding how biases are embedded in a model. We propose LOGAN, a new bias detection technique based on clustering. Experiments on toxicity classification and object classification tasks show that LOGAN identifies bias in a local region.
  arXiv  Detail & Related papers  (2020-10-06T16:42:51Z)
• Towards Robustifying NLI Models Against Lexical Dataset Biases [94.79704960296108]
  This paper explores both data-level and model-level debiasing methods to robustify models against lexical dataset biases. First, we debias the dataset through data augmentation and enhancement, but show that the model bias cannot be fully removed via this method. The second approach employs a bag-of-words sub-model to capture the features that are likely to exploit the bias and prevents the original model from learning these biased features.
  arXiv  Detail & Related papers  (2020-05-10T17:56:10Z)

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.