FAIntbench: A Holistic and Precise Benchmark for Bias Evaluation in Text-to-Image Models

• URL: http://arxiv.org/abs/2405.17814v5
• Date: Wed, 18 Sep 2024 04:40:40 GMT
• Title: FAIntbench: A Holistic and Precise Benchmark for Bias Evaluation in Text-to-Image Models
• Authors: Hanjun Luo, Ziye Deng, Ruizhe Chen, Zuozhu Liu,
• Abstract summary: FAIntbench is a holistic and precise benchmark for biases in Text-to-Image (T2I) models. We applied FAIntbench to evaluate seven recent large-scale T2I models and conducted human evaluation. Results demonstrated the effectiveness of FAIntbench in identifying various biases.
• Score: 7.30796695035169
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The rapid development and reduced barriers to entry for Text-to-Image (T2I) models have raised concerns about the biases in their outputs, but existing research lacks a holistic definition and evaluation framework of biases, limiting the enhancement of debiasing techniques. To address this issue, we introduce FAIntbench, a holistic and precise benchmark for biases in T2I models. In contrast to existing benchmarks that evaluate bias in limited aspects, FAIntbench evaluate biases from four dimensions: manifestation of bias, visibility of bias, acquired attributes, and protected attributes. We applied FAIntbench to evaluate seven recent large-scale T2I models and conducted human evaluation, whose results demonstrated the effectiveness of FAIntbench in identifying various biases. Our study also revealed new research questions about biases, including the side-effect of distillation. The findings presented here are preliminary, highlighting the potential of FAIntbench to advance future research aimed at mitigating the biases in T2I models. Our benchmark is publicly available to ensure the reproducibility.

Related papers

• A Critical Review of Predominant Bias in Neural Networks [19.555188118439883]
  We find that there exists a persistent, extensive but under-explored confusion regarding these two types of biases. We aim to restore clarity by providing two mathematical definitions for these two predominant biases and leveraging these definitions to unify a comprehensive list of papers.
  arXiv  Detail & Related papers  (2025-02-16T07:55:19Z)
• FairT2I: Mitigating Social Bias in Text-to-Image Generation via Large Language Model-Assisted Detection and Attribute Rebalancing [32.01426831450348]
  We introduce FairT2I, a novel framework that harnesses large language models to detect and mitigate social biases in T2I generation. Our results show that FairT2I successfully mitigates social biases and enhances the diversity of sensitive attributes in generated images.
  arXiv  Detail & Related papers  (2025-02-06T07:22:57Z)
• Editable Fairness: Fine-Grained Bias Mitigation in Language Models [52.66450426729818]
  We propose a novel debiasing approach, Fairness Stamp (FAST), which enables fine-grained calibration of individual social biases. FAST surpasses state-of-the-art baselines with superior debiasing performance. 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)
• BIGbench: A Unified Benchmark for Social Bias in Text-to-Image Generative Models Based on Multi-modal LLM [8.24274551090375]
  We introduce BIGbench, a unified benchmark for Biases of Image Generation. Unlike existing benchmarks, BIGbench classifies and evaluates biases across four dimensions. Our study also reveal new research directions about biases, such as the effect of distillation and irrelevant protected attributes.
  arXiv  Detail & Related papers  (2024-07-21T18:09:40Z)
• Thinking Racial Bias in Fair Forgery Detection: Models, Datasets and Evaluations [63.52709761339949]
  We first contribute a dedicated dataset called the Fair Forgery Detection (FairFD) dataset, where we prove the racial bias of public state-of-the-art (SOTA) methods. We design novel metrics including Approach Averaged Metric and Utility Regularized Metric, which can avoid deceptive results. We also present an effective and robust post-processing technique, Bias Pruning with Fair Activations (BPFA), which improves fairness without requiring retraining or weight updates.
  arXiv  Detail & Related papers  (2024-07-19T14:53:18Z)
• VLBiasBench: A Comprehensive Benchmark for Evaluating Bias in Large Vision-Language Model [72.13121434085116]
  We introduce VLBiasBench, a benchmark to evaluate biases in Large Vision-Language Models (LVLMs) VLBiasBench features a dataset that covers nine distinct categories of social biases, including age, disability status, gender, nationality, physical appearance, race, religion, profession, social economic status, as well as two intersectional bias categories: race x gender and race x social economic status. We conduct extensive evaluations on 15 open-source models as well as two advanced closed-source models, yielding new insights into the biases present in these models.
  arXiv  Detail & Related papers  (2024-06-20T10:56:59Z)
• Survey of Bias In Text-to-Image Generation: Definition, Evaluation, and Mitigation [47.770531682802314]
  Even simple prompts could cause T2I models to exhibit conspicuous social bias in generated images. We present the first extensive survey on bias in T2I generative models. We discuss how these works define, evaluate, and mitigate different aspects of bias.
  arXiv  Detail & Related papers  (2024-04-01T10:19:05Z)
• 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)
• 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)

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.