The SAME score: Improved cosine based bias score for word embeddings

• URL: http://arxiv.org/abs/2203.14603v1
• Date: Mon, 28 Mar 2022 09:28:13 GMT
• Title: The SAME score: Improved cosine based bias score for word embeddings
• Authors: Sarah Schr\"oder, Alexander Schulz, Philip Kenneweg, Robert Feldhans, Fabian Hinder, Barbara Hammer
• Abstract summary: We provide a bias definition based on the ideas from the literature and derive novel requirements for bias scores. We propose a new bias score, SAME, to address the shortcomings of existing bias scores and show empirically that SAME is better suited to quantify biases in word embeddings.
• Score: 63.24247894974291
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Over the last years, word and sentence embeddings have established as text preprocessing for all kinds of NLP tasks and improved performances in these tasks significantly. Unfortunately, it has also been shown that these embeddings inherit various kinds of biases from the training data and thereby pass on biases present in society to NLP solutions. Many papers attempted to quantify bias in word or sentence embeddings to evaluate debiasing methods or compare different embedding models, often with cosine-based scores. However, some works have raised doubts about these scores showing that even though they report low biases, biases persist and can be shown with other tests. In fact, there is a great variety of bias scores or tests proposed in the literature without any consensus on the optimal solutions. We lack works that study the behavior of bias scores and elaborate their advantages and disadvantages. In this work, we will explore different cosine-based bias scores. We provide a bias definition based on the ideas from the literature and derive novel requirements for bias scores. Furthermore, we thoroughly investigate the existing cosine-based scores and their limitations in order to show why these scores fail to report biases in some situations. Finally, we propose a new bias score, SAME, to address the shortcomings of existing bias scores and show empirically that SAME is better suited to quantify biases in word embeddings.

Related papers

• Semantic Properties of cosine based bias scores for word embeddings [48.0753688775574]
  We propose requirements for bias scores to be considered meaningful for quantifying biases. We analyze cosine based scores from the literature with regard to these requirements. We underline these findings with experiments to show that the bias scores' limitations have an impact in the application case.
  arXiv  Detail & Related papers  (2024-01-27T20:31:10Z)
• GPTBIAS: A Comprehensive Framework for Evaluating Bias in Large Language Models [83.30078426829627]
  Large language models (LLMs) have gained popularity and are being widely adopted by a large user community. The existing evaluation methods have many constraints, and their results exhibit a limited degree of interpretability. We propose a bias evaluation framework named GPTBIAS that leverages the high performance of LLMs to assess bias in models.
  arXiv  Detail & Related papers  (2023-12-11T12:02:14Z)
• Mitigating Bias for Question Answering Models by Tracking Bias Influence [84.66462028537475]
  We propose BMBI, an approach to mitigate the bias of multiple-choice QA models. Based on the intuition that a model would lean to be more biased if it learns from a biased example, we measure the bias level of a query instance. We show that our method could be applied to multiple QA formulations across multiple bias categories.
  arXiv  Detail & Related papers  (2023-10-13T00:49:09Z)
• Looking at the Overlooked: An Analysis on the Word-Overlap Bias in Natural Language Inference [20.112129592923246]
  We focus on an overlooked aspect of the overlap bias in NLI models: the reverse word-overlap bias. Current NLI models are highly biased towards the non-entailment label on instances with low overlap. We investigate the reasons for the emergence of the overlap bias and the role of minority examples in its mitigation.
  arXiv  Detail & Related papers  (2022-11-07T21:02:23Z)
• No Word Embedding Model Is Perfect: Evaluating the Representation Accuracy for Social Bias in the Media [17.4812995898078]
  We study what kind of embedding algorithm serves best to accurately measure types of social bias known to exist in US online news articles. We collect 500k articles and review psychology literature with respect to expected social bias. We compare how models trained with the algorithms on news articles represent the expected social bias.
  arXiv  Detail & Related papers  (2022-11-07T15:45:52Z)
• The Tail Wagging the Dog: Dataset Construction Biases of Social Bias Benchmarks [75.58692290694452]
  We compare social biases with non-social biases stemming from choices made during dataset construction that might not even be discernible to the human eye. We observe that these shallow modifications have a surprising effect on the resulting degree of bias across various models.
  arXiv  Detail & Related papers  (2022-10-18T17:58:39Z)
• Toward Understanding Bias Correlations for Mitigation in NLP [34.956581421295]
  This work aims to provide a first systematic study toward understanding bias correlations in mitigation. We examine bias mitigation in two common NLP tasks -- toxicity detection and word embeddings. Our findings suggest that biases are correlated and present scenarios in which independent debiasing approaches may be insufficient.
  arXiv  Detail & Related papers  (2022-05-24T22:48:47Z)
• Balancing out Bias: Achieving Fairness Through Training Reweighting [58.201275105195485]
  Bias in natural language processing arises from models learning characteristics of the author such as gender and race. Existing methods for mitigating and measuring bias do not directly account for correlations between author demographics and linguistic variables. This paper introduces a very simple but highly effective method for countering bias using instance reweighting.
  arXiv  Detail & Related papers  (2021-09-16T23:40:28Z)
• Argument from Old Man's View: Assessing Social Bias in Argumentation [20.65183968971417]
  Social bias in language poses a problem with ethical impact for many NLP applications. Recent research has shown that machine learning models trained on respective data may not only adopt, but even amplify the bias. We study the existence of social biases in large English debate portals.
  arXiv  Detail & Related papers  (2020-11-24T10:39:44Z)

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.