AIM-Fair: Advancing Algorithmic Fairness via Selectively Fine-Tuning Biased Models with Contextual Synthetic Data

• URL: http://arxiv.org/abs/2503.05665v1
• Date: Fri, 07 Mar 2025 18:26:48 GMT
• Title: AIM-Fair: Advancing Algorithmic Fairness via Selectively Fine-Tuning Biased Models with Contextual Synthetic Data
• Authors: Zengqun Zhao, Ziquan Liu, Yu Cao, Shaogang Gong, Ioannis Patras,
• Abstract summary: Existing methods often face limitations in the diversity and quality of synthetic data, leading to compromised fairness and overall model accuracy.<n>This paper proposes AIM-Fair, aiming to overcome these limitations and harness the potential of cutting-edge generative models in promoting algorithmic fairness.<n> Experiments on CelebA and UTKFace datasets show that our AIM-Fair improves model fairness while maintaining utility, outperforming both fully and partially fine-tuned approaches to model fairness.
• Score: 44.94133254226272
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent advances in generative models have sparked research on improving model fairness with AI-generated data. However, existing methods often face limitations in the diversity and quality of synthetic data, leading to compromised fairness and overall model accuracy. Moreover, many approaches rely on the availability of demographic group labels, which are often costly to annotate. This paper proposes AIM-Fair, aiming to overcome these limitations and harness the potential of cutting-edge generative models in promoting algorithmic fairness. We investigate a fine-tuning paradigm starting from a biased model initially trained on real-world data without demographic annotations. This model is then fine-tuned using unbiased synthetic data generated by a state-of-the-art diffusion model to improve its fairness. Two key challenges are identified in this fine-tuning paradigm, 1) the low quality of synthetic data, which can still happen even with advanced generative models, and 2) the domain and bias gap between real and synthetic data. To address the limitation of synthetic data quality, we propose Contextual Synthetic Data Generation (CSDG) to generate data using a text-to-image diffusion model (T2I) with prompts generated by a context-aware LLM, ensuring both data diversity and control of bias in synthetic data. To resolve domain and bias shifts, we introduce a novel selective fine-tuning scheme in which only model parameters more sensitive to bias and less sensitive to domain shift are updated. Experiments on CelebA and UTKFace datasets show that our AIM-Fair improves model fairness while maintaining utility, outperforming both fully and partially fine-tuned approaches to model fairness.

Related papers

• Data Augmentation via Diffusion Model to Enhance AI Fairness [1.2979015577834876]
  This paper explores the potential of diffusion models to generate synthetic data to improve AI fairness. The Tabular Denoising Diffusion Probabilistic Model (Tab-DDPM) was utilized with different amounts of generated data for data augmentation. Experimental results demonstrate that the synthetic data generated by Tab-DDPM improves fairness in binary classification.
  arXiv  Detail & Related papers  (2024-10-20T18:52:31Z)
• Improving Fairness and Mitigating MADness in Generative Models [21.024727486615646]
  We show that training generative models with intentionally designed hypernetworks leads to models that are more fair when generating datapoints belonging to minority classes. We introduce a regularization term that penalizes discrepancies between a generative model's estimated weights when trained on real data versus its own synthetic data.
  arXiv  Detail & Related papers  (2024-05-22T20:24:41Z)
• Does Synthetic Data Make Large Language Models More Efficient? [0.0]
  This paper explores the nuances of synthetic data generation in NLP. We highlight its advantages, including data augmentation potential and the introduction of structured variety. We demonstrate the impact of template-based synthetic data on the performance of modern transformer models.
  arXiv  Detail & Related papers  (2023-10-11T19:16:09Z)
• On the Stability of Iterative Retraining of Generative Models on their own Data [56.153542044045224]
  We study the impact of training generative models on mixed datasets. We first prove the stability of iterative training under the condition that the initial generative models approximate the data distribution well enough. We empirically validate our theory on both synthetic and natural images by iteratively training normalizing flows and state-of-the-art diffusion models.
  arXiv  Detail & Related papers  (2023-09-30T16:41:04Z)
• Synthetic data, real errors: how (not) to publish and use synthetic data [86.65594304109567]
  We show how the generative process affects the downstream ML task. We introduce Deep Generative Ensemble (DGE) to approximate the posterior distribution over the generative process model parameters.
  arXiv  Detail & Related papers  (2023-05-16T07:30:29Z)
• Non-Invasive Fairness in Learning through the Lens of Data Drift [88.37640805363317]
  We show how to improve the fairness of Machine Learning models without altering the data or the learning algorithm. We use a simple but key insight: the divergence of trends between different populations, and, consecutively, between a learned model and minority populations, is analogous to data drift. We explore two strategies (model-splitting and reweighing) to resolve this drift, aiming to improve the overall conformance of models to the underlying data.
  arXiv  Detail & Related papers  (2023-03-30T17:30:42Z)
• DECAF: Generating Fair Synthetic Data Using Causally-Aware Generative Networks [71.6879432974126]
  We introduce DECAF: a GAN-based fair synthetic data generator for tabular data. We show that DECAF successfully removes undesired bias and is capable of generating high-quality synthetic data. We provide theoretical guarantees on the generator's convergence and the fairness of downstream models.
  arXiv  Detail & Related papers  (2021-10-25T12:39:56Z)
• TabFairGAN: Fair Tabular Data Generation with Generative Adversarial Networks [0.0]
  We propose a Generative Adversarial Network for tabular data generation. We test our results in both cases of unconstrained, and constrained fair data generation. Our model is comparably more stable by using only one critic, and also by avoiding major problems of original GAN model.
  arXiv  Detail & Related papers  (2021-09-02T01:48:01Z)
• Transitioning from Real to Synthetic data: Quantifying the bias in model [1.6134566438137665]
  This study aims to establish a trade-off between bias and fairness in the models trained using synthetic data. We demonstrate there exist a varying levels of bias impact on models trained using synthetic data.
  arXiv  Detail & Related papers  (2021-05-10T06:57:14Z)
• Representative & Fair Synthetic Data [68.8204255655161]
  We present a framework to incorporate fairness constraints into the self-supervised learning process. We generate a representative as well as fair version of the UCI Adult census data set. We consider representative & fair synthetic data a promising future building block to teach algorithms not on historic worlds, but rather on the worlds that we strive to live in.
  arXiv  Detail & Related papers  (2021-04-07T09:19:46Z)

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.