Assessing the quality and coherence of word embeddings after SCM-based intersectional bias mitigation

• URL: http://arxiv.org/abs/2601.04393v1
• Date: Wed, 07 Jan 2026 21:03:53 GMT
• Title: Assessing the quality and coherence of word embeddings after SCM-based intersectional bias mitigation
• Abstract summary: We study three widely used embedding families (Word2Vec, GloVe, and ConceptNet Numberbatch)<n>We study three debiasing strategies: Subtraction, Linear Projection, and Partial Projection.<n>Findings suggest intersectional debiasing with Stereotype Content Model is practical in static embed- dings.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Static word embeddings often absorb social biases from the text they learn from, and those biases can quietly shape downstream systems. Prior work that uses the Stereotype Content Model (SCM) has focused mostly on single-group bias along warmth and competence. We broaden that lens to intersectional bias by building compound representations for pairs of social identities through summation or concatenation, and by applying three debiasing strategies: Subtraction, Linear Projection, and Partial Projection. We study three widely used embedding families (Word2Vec, GloVe, and ConceptNet Numberbatch) and assess them with two complementary views of utility: whether local neighborhoods remain coherent and whether analogy behavior is preserved. Across models, SCM-based mitigation carries over well to the intersectional case and largely keeps the overall semantic landscape intact. The main cost is a familiar trade off: methods that most tightly preserve geometry tend to be more cautious about analogy behavior, while more assertive projections can improve analogies at the expense of strict neighborhood stability. Partial Projection is reliably conservative and keeps representations steady; Linear Projection can be more assertive; Subtraction is a simple baseline that remains competitive. The choice between summation and concatenation depends on the embedding family and the application goal. Together, these findings suggest that intersectional debiasing with SCM is practical in static embed- dings, and they offer guidance for selecting aggregation and debiasing settings when balancing stability against analogy performance.

Related papers

• To Compare, or Not to Compare: On Methodological Practices in Evaluating Social Bias [61.40435736418359]
  We introduce a unified framework that standardizes heterogeneous benchmarks to contrast isolated demographic assessments with forced-choice comparative settings.<n>We show that comparative settings act as aggressive catalysts for latent discrimination, a shift primarily driven by underspecified contexts.<n>Finally, we demonstrate that this comparative prejudice is a generalized phenomenon that scales positively with model size.
  arXiv  Detail & Related papers  (2026-06-23T13:53:50Z)
• Dual-Branch Cross-Projection Debiasing through Diffusion-based Disentanglement [66.67463557274358]
  Foundation models trained on biased datasets often rely on spurious correlations between target labels and non-causal attributes.<n>We introduce Confidence-guided Concept Mining (CBCM) to identify reliable spurious attributes without attribute annotations.<n>We propose Dual-branch Cross-projection Debiasing (DCD), a prompt-tuning framework that separates target and spurious representations into two branches.
  arXiv  Detail & Related papers  (2026-06-23T05:28:47Z)
• Dynamics Within Latent Chain-of-Thought: An Empirical Study of Causal Structure [58.89643769707751]
  We study latent chain-of-thought as a manipulable causal process in representation space.<n>We find that latent-step budgets behave less like homogeneous extra depth and more like staged functionality with non-local routing.<n>These results motivate mode-conditional and stability-aware analyses as more reliable tools for interpreting and improving latent reasoning systems.
  arXiv  Detail & Related papers  (2026-02-09T15:25:12Z)
• Invariance on Manifolds: Understanding Robust Visual Representations for Place Recognition [19.200074425090595]
  We propose a Second-Order Geometric Statistics framework that inherently captures geometric stability without training.<n>Our approach introduces a training-free framework built upon fixed, pre-trained backbones, achieving strong zero-shot generalization without parameter updates.
  arXiv  Detail & Related papers  (2026-01-31T18:12:29Z)
• Bias Is a Subspace, Not a Coordinate: A Geometric Rethinking of Post-hoc Debiasing in Vision-Language Models [13.46898179372249]
  Vision-Language Models (VLMs) have become indispensable for multimodal reasoning, yet their representations often encode and amplify demographic biases.<n>We propose a geometrically principled framework that identifies and removes the entire subspace of linearly decodable bias.<n>Our method achieves more robust debiasing with an average improvement of $18.5%$ across four fairness metrics.
  arXiv  Detail & Related papers  (2025-11-22T17:04:30Z)
• TriCon-Fair: Triplet Contrastive Learning for Mitigating Social Bias in Pre-trained Language Models [14.709063635156054]
  TriCon-Fair is a contrastive learning framework that employs a decoupled loss that combines triplet and language modeling terms to eliminate positive-negative coupling.<n> Experimental results demonstrate that TriCon-Fair reduces discriminatory output beyond existing debiasing baselines.
  arXiv  Detail & Related papers  (2025-11-02T08:37:56Z)
• Beacon: Single-Turn Diagnosis and Mitigation of Latent Sycophancy in Large Language Models [0.0]
  Large language models internalize a structural trade-off between truthfulness and obsequious flattery.<n>This latent bias, known as sycophancy, manifests as a preference for user agreement over principled reasoning.<n>We introduce Beacon, a single-turn forced-choice benchmark that isolates this bias independent of conversational context.
  arXiv  Detail & Related papers  (2025-10-19T06:36:57Z)
• APFEx: Adaptive Pareto Front Explorer for Intersectional Fairness [16.993547305381327]
  We introduce APFEx, the first framework to explicitly model intersectional fairness as a joint optimization problem.<n>APFEx combines adaptive multi-objectives, gradient weighting, and exploration strategies to navigate fairness-accuracy trade-offs.<n>Experiments on four real-world datasets demonstrate APFEx's superiority, reducing fairness violations while maintaining competitive accuracy.
  arXiv  Detail & Related papers  (2025-09-17T11:13:22Z)
• Enhancing the Robustness of Contextual ASR to Varying Biasing Information Volumes Through Purified Semantic Correlation Joint Modeling [63.755562174967274]
  Cross-attention is affected by variations in biasing information volume.<n>We propose a purified semantic correlation joint modeling (PSC-Joint) approach.<n> PSC-Joint achieves average relative F1 score improvements of up to 21.34% on AISHELL-1 and 28.46% on KeSpeech.
  arXiv  Detail & Related papers  (2025-09-07T03:46:59Z)
• Mitigate One, Skew Another? Tackling Intersectional Biases in Text-to-Image Models [73.20190633746442]
  We introduce BiasConnect, a novel tool for analyzing and quantifying bias interactions in text-to-image models.<n>We propose InterMit, an intersectional bias mitigation algorithm guided by user-defined target distributions and priority weights.
  arXiv  Detail & Related papers  (2025-05-22T20:56:38Z)
• Evaluate Bias without Manual Test Sets: A Concept Representation Perspective for LLMs [25.62533031580287]
  Bias in Large Language Models (LLMs) significantly undermines their reliability and fairness.<n>We propose BiasLens, a test-set-free bias analysis framework based on the structure of the model's vector space.
  arXiv  Detail & Related papers  (2025-05-21T13:50:23Z)
• Fairness Mediator: Neutralize Stereotype Associations to Mitigate Bias in Large Language Models [66.5536396328527]
  LLMs inadvertently absorb spurious correlations from training data, leading to stereotype associations between biased concepts and specific social groups.<n>We propose Fairness Mediator (FairMed), a bias mitigation framework that neutralizes stereotype associations.<n>Our framework comprises two main components: a stereotype association prober and an adversarial debiasing neutralizer.
  arXiv  Detail & Related papers  (2025-04-10T14:23:06Z)
• BiasConnect: Investigating Bias Interactions in Text-to-Image Models [73.76853483463836]
  We introduce BiasConnect, a novel tool designed to analyze and quantify bias interactions in Text-to-Image models.<n>Our method provides empirical estimates that indicate how other bias dimensions shift toward or away from an ideal distribution when a given bias is modified.<n>We demonstrate the utility of BiasConnect for selecting optimal bias mitigation axes, comparing different TTI models on the dependencies they learn, and understanding the amplification of intersectional societal biases in TTI models.
  arXiv  Detail & Related papers  (2025-03-12T19:01:41Z)
• Beyond Instance Discrimination: Relation-aware Contrastive Self-supervised Learning [75.46664770669949]
  We present relation-aware contrastive self-supervised learning (ReCo) to integrate instance relations. Our ReCo consistently gains remarkable performance improvements.
  arXiv  Detail & Related papers  (2022-11-02T03:25:28Z)
• Learning Equilibria in Matching Markets from Bandit Feedback [139.29934476625488]
  We develop a framework and algorithms for learning stable market outcomes under uncertainty. Our work takes a first step toward elucidating when and how stable matchings arise in large, data-driven marketplaces.
  arXiv  Detail & Related papers  (2021-08-19T17:59:28Z)

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.