Inducing Group Fairness in LLM-Based Decisions

• URL: http://arxiv.org/abs/2406.16738v1
• Date: Mon, 24 Jun 2024 15:45:20 GMT
• Title: Inducing Group Fairness in LLM-Based Decisions
• Authors: James Atwood, Preethi Lahoti, Ananth Balashankar, Flavien Prost, Ahmad Beirami,
• Abstract summary: Group fairness in Prompting Large Language Models (LLMs) is a well-studied problem. We show that prompt-based classifiers may lead to unfair decisions. We introduce several remediation techniques and benchmark their fairness and performance trade-offs.
• Score: 12.368678951470162
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Prompting Large Language Models (LLMs) has created new and interesting means for classifying textual data. While evaluating and remediating group fairness is a well-studied problem in classifier fairness literature, some classical approaches (e.g., regularization) do not carry over, and some new opportunities arise (e.g., prompt-based remediation). We measure fairness of LLM-based classifiers on a toxicity classification task, and empirically show that prompt-based classifiers may lead to unfair decisions. We introduce several remediation techniques and benchmark their fairness and performance trade-offs. We hope our work encourages more research on group fairness in LLM-based classifiers.

Related papers

• Studying Classifier(-Free) Guidance From a Classifier-Centric Perspective [100.54185280153753]
  We find that both classifier guidance and classifier-free guidance achieve conditional generation by pushing the denoising diffusion trajectories away from decision boundaries. We propose a generic postprocessing step built upon flow-matching to shrink the gap between the learned distribution for a pretrained denoising diffusion model and the real data distribution.
  arXiv  Detail & Related papers  (2025-03-13T17:59:59Z)
• Adaptive Margin Global Classifier for Exemplar-Free Class-Incremental Learning [3.4069627091757178]
  Existing methods mainly focus on handling biased learning. We introduce a Distribution-Based Global (DBGC) to avoid bias factors in existing methods, such as data imbalance and sampling. More importantly, the compromised distributions of old classes are simulated via a simple operation, variance (VE). This loss is proven equivalent to an Adaptive Margin Softmax Cross Entropy (AMarX)
  arXiv  Detail & Related papers  (2024-09-20T07:07:23Z)
• Fairness in Large Language Models in Three Hours [2.443957114877221]
  This tutorial provides a systematic overview of recent advances in the literature concerning large language models. The concept of fairness in LLMs is then explored, summarizing the strategies for evaluating bias and the algorithms designed to promote fairness.
  arXiv  Detail & Related papers  (2024-08-02T03:44:14Z)
• Decompose and Aggregate: A Step-by-Step Interpretable Evaluation Framework [75.81096662788254]
  Large Language Models (LLMs) are scalable and economical evaluators. The question of how reliable these evaluators are has emerged as a crucial research question. We propose Decompose and Aggregate, which breaks down the evaluation process into different stages based on pedagogical practices.
  arXiv  Detail & Related papers  (2024-05-24T08:12:30Z)
• Fairness in Large Language Models: A Taxonomic Survey [2.669847575321326]
  Large Language Models (LLMs) have demonstrated remarkable success across various domains. Despite their promising performance in numerous real-world applications, most of these algorithms lack fairness considerations.
  arXiv  Detail & Related papers  (2024-03-31T22:22:53Z)
• Few-Shot Fairness: Unveiling LLM's Potential for Fairness-Aware Classification [7.696798306913988]
  We introduce a framework outlining fairness regulations aligned with various fairness definitions. We explore the configuration for in-context learning and the procedure for selecting in-context demonstrations using RAG. Experiments conducted with different LLMs indicate that GPT-4 delivers superior results in terms of both accuracy and fairness compared to other models.
  arXiv  Detail & Related papers  (2024-02-28T17:29:27Z)
• A Group Fairness Lens for Large Language Models [34.0579082699443]
  Large language models can perpetuate biases and unfairness when deployed in social media contexts. We propose evaluating LLM biases from a group fairness lens using a novel hierarchical schema characterizing diverse social groups. We pioneer a novel chain-of-thought method GF-Think to mitigate biases of LLMs from a group fairness perspective.
  arXiv  Detail & Related papers  (2023-12-24T13:25:15Z)
• Selecting Shots for Demographic Fairness in Few-Shot Learning with Large Language Models [14.772568847965408]
  We explore the effect of shots, which directly affect the performance of models, on the fairness of large language models (LLMs) as NLP classification systems. We consider how different shot selection strategies, both existing and new demographically sensitive methods, affect model fairness across three standard fairness datasets.
  arXiv  Detail & Related papers  (2023-11-14T19:02:03Z)
• Bias and Fairness in Large Language Models: A Survey [73.87651986156006]
  We present a comprehensive survey of bias evaluation and mitigation techniques for large language models (LLMs) We first consolidate, formalize, and expand notions of social bias and fairness in natural language processing. We then unify the literature by proposing three intuitive, two for bias evaluation, and one for mitigation.
  arXiv  Detail & Related papers  (2023-09-02T00:32:55Z)
• Fair Few-shot Learning with Auxiliary Sets [53.30014767684218]
  In many machine learning (ML) tasks, only very few labeled data samples can be collected, which can lead to inferior fairness performance. In this paper, we define the fairness-aware learning task with limited training samples as the emphfair few-shot learning problem. We devise a novel framework that accumulates fairness-aware knowledge across different meta-training tasks and then generalizes the learned knowledge to meta-test tasks.
  arXiv  Detail & Related papers  (2023-08-28T06:31:37Z)
• FFB: A Fair Fairness Benchmark for In-Processing Group Fairness Methods [84.1077756698332]
  This paper introduces the Fair Fairness Benchmark (textsfFFB), a benchmarking framework for in-processing group fairness methods. We provide a comprehensive analysis of state-of-the-art methods to ensure different notions of group fairness.
  arXiv  Detail & Related papers  (2023-06-15T19:51:28Z)
• Learning Context-aware Classifier for Semantic Segmentation [88.88198210948426]
  In this paper, contextual hints are exploited via learning a context-aware classifier. Our method is model-agnostic and can be easily applied to generic segmentation models. With only negligible additional parameters and +2% inference time, decent performance gain has been achieved on both small and large models.
  arXiv  Detail & Related papers  (2023-03-21T07:00:35Z)
• Open World Classification with Adaptive Negative Samples [89.2422451410507]
  Open world classification is a task in natural language processing with key practical relevance and impact. We propose an approach based on underlineadaptive underlinesamples (ANS) designed to generate effective synthetic open category samples in the training stage. ANS achieves significant improvements over state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-03-09T21:12:46Z)
• Learning Informative Representation for Fairness-aware Multivariate Time-series Forecasting: A Group-based Perspective [50.093280002375984]
  Performance unfairness among variables widely exists in multivariate time series (MTS) forecasting models. We propose a novel framework, named FairFor, for fairness-aware MTS forecasting.
  arXiv  Detail & Related papers  (2023-01-27T04:54:12Z)
• Anomaly Detection using Ensemble Classification and Evidence Theory [62.997667081978825]
  We present a novel approach for novel detection using ensemble classification and evidence theory. A pool selection strategy is presented to build a solid ensemble classifier. We use uncertainty for the anomaly detection approach.
  arXiv  Detail & Related papers  (2022-12-23T00:50:41Z)
• Fair Group-Shared Representations with Normalizing Flows [68.29997072804537]
  We develop a fair representation learning algorithm which is able to map individuals belonging to different groups in a single group. We show experimentally that our methodology is competitive with other fair representation learning algorithms.
  arXiv  Detail & Related papers  (2022-01-17T10:49:49Z)
• Multiple Classifiers Based Maximum Classifier Discrepancy for Unsupervised Domain Adaptation [25.114533037440896]
  We propose to extend the structure of two classifiers to multiple classifiers to further boost its performance. We demonstrate that, on average, adopting the structure of three classifiers normally yields the best performance as a trade-off between the accuracy and efficiency.
  arXiv  Detail & Related papers  (2021-08-02T03:00:13Z)
• MCDAL: Maximum Classifier Discrepancy for Active Learning [74.73133545019877]
  Recent state-of-the-art active learning methods have mostly leveraged Generative Adversarial Networks (GAN) for sample acquisition. We propose in this paper a novel active learning framework that we call Maximum Discrepancy for Active Learning (MCDAL) In particular, we utilize two auxiliary classification layers that learn tighter decision boundaries by maximizing the discrepancies among them.
  arXiv  Detail & Related papers  (2021-07-23T06:57:08Z)
• Early Exiting with Ensemble Internal Classifiers [57.80488632985445]
  Early exiting has gained much attention in the NLP community. We propose a voting-based strategy that considers predictions of all the past internal classifiers to infer the correct label. Experimental results on various NLP tasks show that our proposed objective function and voting-based strategy can achieve better accuracy-speed trade-offs.
  arXiv  Detail & Related papers  (2021-05-28T12:54:11Z)

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.