Investigating the Capabilities and Limitations of Machine Learning for Identifying Bias in English Language Data with Information and Heritage Professionals

• URL: http://arxiv.org/abs/2504.00860v1
• Date: Tue, 01 Apr 2025 14:51:25 GMT
• Title: Investigating the Capabilities and Limitations of Machine Learning for Identifying Bias in English Language Data with Information and Heritage Professionals
• Authors: Lucy Havens, Benjamin Bach, Melissa Terras, Beatrice Alex,
• Abstract summary: We show that predominant ML approaches assume bias can be removed and fair models can be created.<n>We create models to identify biased language, drawing attention to a dataset's biases rather than trying to remove them.
• Score: 13.622709812029946
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Despite numerous efforts to mitigate their biases, ML systems continue to harm already-marginalized people. While predominant ML approaches assume bias can be removed and fair models can be created, we show that these are not always possible, nor desirable, goals. We reframe the problem of ML bias by creating models to identify biased language, drawing attention to a dataset's biases rather than trying to remove them. Then, through a workshop, we evaluated the models for a specific use case: workflows of information and heritage professionals. Our findings demonstrate the limitations of ML for identifying bias due to its contextual nature, the way in which approaches to mitigating it can simultaneously privilege and oppress different communities, and its inevitability. We demonstrate the need to expand ML approaches to bias and fairness, providing a mixed-methods approach to investigating the feasibility of removing bias or achieving fairness in a given ML use case.

Related papers

• Cognitive Debiasing Large Language Models for Decision-Making [71.2409973056137]
  Large language models (LLMs) have shown potential in supporting decision-making applications. We propose a cognitive debiasing approach, called self-debiasing, that enhances the reliability of LLMs. Our method follows three sequential steps -- bias determination, bias analysis, and cognitive debiasing -- to iteratively mitigate potential cognitive biases in prompts.
  arXiv  Detail & Related papers  (2025-04-05T11:23:05Z)
• Towards Resource Efficient and Interpretable Bias Mitigation in Large Language Models [1.787433808079955]
  Large language models (LLMs) have been observed to perpetuate unwanted biases in training data.<n>In this paper, we mitigate bias by leveraging small biased and anti-biased expert models to obtain a debiasing signal.<n> Experiments on mitigating gender, race, and religion biases show a reduction in bias on several local and global bias metrics.
  arXiv  Detail & Related papers  (2024-12-02T16:56:08Z)
• Promoting Equality in Large Language Models: Identifying and Mitigating the Implicit Bias based on Bayesian Theory [29.201402717025335]
  Large language models (LLMs) are trained on extensive text corpora, which inevitably include biased information. We have formally defined the implicit bias problem and developed an innovative framework for bias removal based on Bayesian theory.
  arXiv  Detail & Related papers  (2024-08-20T07:40:12Z)
• REFINE-LM: Mitigating Language Model Stereotypes via Reinforcement Learning [18.064064773660174]
  We introduce REFINE-LM, a debiasing method that uses reinforcement learning to handle different types of biases without any fine-tuning. By training a simple model on top of the word probability distribution of a LM, our bias reinforcement learning method enables model debiasing without human annotations. Experiments conducted on a wide range of models, including several LMs, show that our method significantly reduces stereotypical biases while preserving LMs performance.
  arXiv  Detail & Related papers  (2024-08-18T14:08:31Z)
• Identifying and Mitigating Social Bias Knowledge in Language Models [52.52955281662332]
  We propose a novel debiasing approach, Fairness Stamp (FAST), which enables fine-grained calibration of individual social biases.<n>FAST surpasses state-of-the-art baselines with superior debiasing performance.<n>This highlights the potential of fine-grained debiasing strategies to achieve fairness in large language models.
  arXiv  Detail & Related papers  (2024-08-07T17:14:58Z)
• Self-Debiasing Large Language Models: Zero-Shot Recognition and Reduction of Stereotypes [73.12947922129261]
  We leverage the zero-shot capabilities of large language models to reduce stereotyping. We show that self-debiasing can significantly reduce the degree of stereotyping across nine different social groups. We hope this work opens inquiry into other zero-shot techniques for bias mitigation.
  arXiv  Detail & Related papers  (2024-02-03T01:40:11Z)
• Fast Model Debias with Machine Unlearning [54.32026474971696]
  Deep neural networks might behave in a biased manner in many real-world scenarios. Existing debiasing methods suffer from high costs in bias labeling or model re-training. We propose a fast model debiasing framework (FMD) which offers an efficient approach to identify, evaluate and remove biases.
  arXiv  Detail & Related papers  (2023-10-19T08:10:57Z)
• In-Contextual Gender Bias Suppression for Large Language Models [47.246504807946884]
  Large Language Models (LLMs) have been reported to encode worrying-levels of gender biases. We propose bias suppression that prevents biased generations of LLMs by providing preambles constructed from manually designed templates. We find that bias suppression has acceptable adverse effect on downstream task performance with HellaSwag and COPA.
  arXiv  Detail & Related papers  (2023-09-13T18:39:08Z)
• Disembodied Machine Learning: On the Illusion of Objectivity in NLP [21.169778613387827]
  We argue that addressing and mitigating biases is near-impossible. We find the prevalent discourse of bias limiting in its ability to address social marginalisation. We recommend to be conscientious of this, and to accept that de-biasing methods only correct for a fraction of biases.
  arXiv  Detail & Related papers  (2021-01-28T12:58:39Z)
• Learning from others' mistakes: Avoiding dataset biases without modeling them [111.17078939377313]
  State-of-the-art natural language processing (NLP) models often learn to model dataset biases and surface form correlations instead of features that target the intended task. Previous work has demonstrated effective methods to circumvent these issues when knowledge of the bias is available. We show a method for training models that learn to ignore these problematic correlations.
  arXiv  Detail & Related papers  (2020-12-02T16:10:54Z)

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.