No Word Embedding Model Is Perfect: Evaluating the Representation Accuracy for Social Bias in the Media

• URL: http://arxiv.org/abs/2211.03634v1
• Date: Mon, 7 Nov 2022 15:45:52 GMT
• Title: No Word Embedding Model Is Perfect: Evaluating the Representation Accuracy for Social Bias in the Media
• Authors: Maximilian Splieth\"over, Maximilian Keiff, Henning Wachsmuth
• Abstract summary: 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.
• Score: 17.4812995898078
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: News articles both shape and reflect public opinion across the political spectrum. Analyzing them for social bias can thus provide valuable insights, such as prevailing stereotypes in society and the media, which are often adopted by NLP models trained on respective data. Recent work has relied on word embedding bias measures, such as WEAT. However, several representation issues of embeddings can harm the measures' accuracy, including low-resource settings and token frequency differences. In this work, we study what kind of embedding algorithm serves best to accurately measure types of social bias known to exist in US online news articles. To cover the whole spectrum of political bias in the US, we collect 500k articles and review psychology literature with respect to expected social bias. We then quantify social bias using WEAT along with embedding algorithms that account for the aforementioned issues. We compare how models trained with the algorithms on news articles represent the expected social bias. Our results suggest that the standard way to quantify bias does not align well with knowledge from psychology. While the proposed algorithms reduce the~gap, they still do not fully match the literature.

Related papers

• DocNet: Semantic Structure in Inductive Bias Detection Models [0.4779196219827508]
  In this paper, we explore an often overlooked aspect of bias detection in documents: the semantic structure of news articles. We present DocNet, a novel, inductive, and low-resource document embedding and bias detection model. We also demonstrate that the semantic structure of news articles from opposing partisan sides, as represented in document-level graph embeddings, have significant similarities.
  arXiv  Detail & Related papers  (2024-06-16T14:51:12Z)
• Quantifying Bias in Text-to-Image Generative Models [49.60774626839712]
  Bias in text-to-image (T2I) models can propagate unfair social representations and may be used to aggressively market ideas or push controversial agendas. Existing T2I model bias evaluation methods only focus on social biases. We propose an evaluation methodology to quantify general biases in T2I generative models, without any preconceived notions.
  arXiv  Detail & Related papers  (2023-12-20T14:26:54Z)
• It's All Relative: Interpretable Models for Scoring Bias in Documents [10.678219157857946]
  We propose an interpretable model to score the bias present in web documents, based only on their textual content. Our model incorporates assumptions reminiscent of the Bradley-Terry axioms and is trained on pairs of revisions of the same Wikipedia article. We show that we can interpret the parameters of the trained model to discover the words most indicative of bias.
  arXiv  Detail & Related papers  (2023-07-16T19:35:38Z)
• Unveiling the Hidden Agenda: Biases in News Reporting and Consumption [59.55900146668931]
  We build a six-year dataset on the Italian vaccine debate and adopt a Bayesian latent space model to identify narrative and selection biases. We found a nonlinear relationship between biases and engagement, with higher engagement for extreme positions. Analysis of news consumption on Twitter reveals common audiences among news outlets with similar ideological positions.
  arXiv  Detail & Related papers  (2023-01-14T18:58:42Z)
• 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)
• The SAME score: Improved cosine based bias score for word embeddings [63.24247894974291]
  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.
  arXiv  Detail & Related papers  (2022-03-28T09:28:13Z)
• Evaluating Metrics for Bias in Word Embeddings [64.55554083622258]
  We formalize a bias definition based on the ideas from previous works and derive conditions for bias metrics. We propose a new metric, SAME, to address the shortcomings of existing metrics and mathematically prove that SAME behaves appropriately.
  arXiv  Detail & Related papers  (2021-11-15T16:07:15Z)
• 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)
• "Thy algorithm shalt not bear false witness": An Evaluation of Multiclass Debiasing Methods on Word Embeddings [3.0204693431381515]
  The paper investigates the state-of-the-art multiclass debiasing techniques: Hard debiasing, SoftWEAT debiasing and Conceptor debiasing. It evaluates their performance when removing religious bias on a common basis by quantifying bias removal via the Word Embedding Association Test (WEAT), Mean Average Cosine Similarity (MAC) and the Relative Negative Sentiment Bias (RNSB)
  arXiv  Detail & Related papers  (2020-10-30T12:49:39Z)
• Robustness and Reliability of Gender Bias Assessment in Word Embeddings: The Role of Base Pairs [23.574442657224008]
  It has been shown that word embeddings can exhibit gender bias, and various methods have been proposed to quantify this. Previous work has leveraged gender word pairs to measure bias and extract biased analogies. We show that the reliance on these gendered pairs has strong limitations. In particular, the well-known analogy "man is to computer-programmer as woman is to homemaker" is due to word similarity rather than societal bias.
  arXiv  Detail & Related papers  (2020-10-06T16:09:05Z)
• Towards Debiasing Sentence Representations [109.70181221796469]
  We show that Sent-Debias is effective in removing biases, and at the same time, preserves performance on sentence-level downstream tasks. We hope that our work will inspire future research on characterizing and removing social biases from widely adopted sentence representations for fairer NLP.
  arXiv  Detail & Related papers  (2020-07-16T04:22:30Z)

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.