Understanding Fairness-Accuracy Trade-offs in Machine Learning Models: Does Promoting Fairness Undermine Performance?

• URL: http://arxiv.org/abs/2411.17374v2
• Date: Wed, 27 Aug 2025 14:36:57 GMT
• Title: Understanding Fairness-Accuracy Trade-offs in Machine Learning Models: Does Promoting Fairness Undermine Performance?
• Authors: Junhua Liu, Roy Ka-Wei Lee, Kwan Hui Lim,
• Abstract summary: We examine fairness using a real-world university admissions dataset comprising 870 applicant profiles.<n>Our analysis reveals that ML models surpass human evaluators in fairness consistency by margins ranging from 14.08% to 18.79%.
• Score: 20.681144764497247
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Fairness in both Machine Learning (ML) predictions and human decision-making is essential, yet both are susceptible to different forms of bias, such as algorithmic and data-driven in ML, and cognitive or subjective in humans. In this study, we examine fairness using a real-world university admissions dataset comprising 870 applicant profiles, leveraging three ML models: XGB, Bi-LSTM, and KNN, alongside BERT embeddings for textual features. To evaluate individual fairness, we introduce a consistency metric that quantifies agreement in decisions among ML models and human experts with diverse backgrounds. Our analysis reveals that ML models surpass human evaluators in fairness consistency by margins ranging from 14.08\% to 18.79\%. Our findings highlight the potential of using ML to enhance fairness in admissions while maintaining high accuracy, advocating a hybrid approach combining human judgement and ML models.

Related papers

• FairReason: Balancing Reasoning and Social Bias in MLLMs [54.26091556079722]
  Multimodal Large Language Models (MLLMs) already achieve state-of-the-art results across a wide range of tasks and modalities.<n>Recent studies explore advanced prompting schemes and post-training fine-tuning to push their reasoning ability further.
  arXiv  Detail & Related papers  (2025-07-30T19:57:22Z)
• Investigating the Capabilities and Limitations of Machine Learning for Identifying Bias in English Language Data with Information and Heritage Professionals [13.622709812029946]
  We show that predominant ML approaches assume bias can be removed and fair models can be created. We create models to identify biased language, drawing attention to a dataset's biases rather than trying to remove them.
  arXiv  Detail & Related papers  (2025-04-01T14:51:25Z)
• Perceived Fairness of the Machine Learning Development Process: Concept Scale Development [0.0]
  unfairness is triggered due to bias in the data, the data curation process, erroneous assumptions, and implicit bias rendered during the development process.<n>We propose operational attributes of perceived fairness to be transparency, accountability, and representativeness.<n>The multidimensional framework for perceived fairness offers a comprehensive understanding of perceived fairness.
  arXiv  Detail & Related papers  (2025-01-23T06:51:31Z)
• Understanding trade-offs in classifier bias with quality-diversity optimization: an application to talent management [2.334978724544296]
  A major struggle for the development of fair AI models lies in the bias implicit in the data available to train such models. We propose a method for visualizing the biases inherent in a dataset and understanding the potential trade-offs between fairness and accuracy.
  arXiv  Detail & Related papers  (2024-11-25T22:14:02Z)
• From Efficiency to Equity: Measuring Fairness in Preference Learning [3.2132738637761027]
  We evaluate fairness in preference learning models inspired by economic theories of inequality and Rawlsian justice. We propose metrics adapted from the Gini Coefficient, Atkinson Index, and Kuznets Ratio to quantify fairness in these models.
  arXiv  Detail & Related papers  (2024-10-24T15:25:56Z)
• GenderCARE: A Comprehensive Framework for Assessing and Reducing Gender Bias in Large Language Models [73.23743278545321]
  Large language models (LLMs) have exhibited remarkable capabilities in natural language generation, but have also been observed to magnify societal biases. GenderCARE is a comprehensive framework that encompasses innovative Criteria, bias Assessment, Reduction techniques, and Evaluation metrics.
  arXiv  Detail & Related papers  (2024-08-22T15:35:46Z)
• 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)
• LLMs instead of Human Judges? A Large Scale Empirical Study across 20 NLP Evaluation Tasks [106.09361690937618]
  There is an increasing trend towards evaluating NLP models with LLMs instead of human judgments.<n>We provide JUDGE-BENCH, a collection of 20 NLP datasets with human annotations covering a broad range of evaluated properties and types of data.<n>We evaluate 11 current LLMs, covering both open-weight and proprietary models, for their ability to replicate the annotations.
  arXiv  Detail & Related papers  (2024-06-26T14:56:13Z)
• A Dynamic Model of Performative Human-ML Collaboration: Theory and Empirical Evidence [2.498836880652668]
  We present a novel framework for thinking about the deployment of machine learning models in a performative, human-ML collaborative system. In our framework, the introduction of ML recommendations changes the data-generating process of human decisions. We find that for many levels of ML performance, humans can improve upon the ML predictions.
  arXiv  Detail & Related papers  (2024-05-22T15:38:30Z)
• Procedural Fairness in Machine Learning [8.034966749391902]
  We first define the procedural fairness of ML models, and then give formal definitions of individual and group procedural fairness. We propose a novel metric to evaluate the group procedural fairness of ML models, called $GPF_FAE$.
  arXiv  Detail & Related papers  (2024-04-02T12:05:02Z)
• 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)
• Fairness-enhancing mixed effects deep learning improves fairness on in- and out-of-distribution clustered (non-iid) data [6.596656267996196]
  We introduce the Fair Mixed Effects Deep Learning (Fair MEDL) framework. Fair MEDL quantifies cluster-invariant fixed effects (FE) and cluster-specific random effects (RE) We incorporate adversarial debiasing to promote fairness across three key metrics: Equalized Odds, Demographic Parity, and Counterfactual Fairness.
  arXiv  Detail & Related papers  (2023-10-04T20:18:45Z)
• 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)
• Learning for Counterfactual Fairness from Observational Data [62.43249746968616]
  Fairness-aware machine learning aims to eliminate biases of learning models against certain subgroups described by certain protected (sensitive) attributes such as race, gender, and age. A prerequisite for existing methods to achieve counterfactual fairness is the prior human knowledge of the causal model for the data. In this work, we address the problem of counterfactually fair prediction from observational data without given causal models by proposing a novel framework CLAIRE.
  arXiv  Detail & Related papers  (2023-07-17T04:08:29Z)
• 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)
• DualFair: Fair Representation Learning at Both Group and Individual Levels via Contrastive Self-supervision [73.80009454050858]
  This work presents a self-supervised model, called DualFair, that can debias sensitive attributes like gender and race from learned representations. Our model jointly optimize for two fairness criteria - group fairness and counterfactual fairness.
  arXiv  Detail & Related papers  (2023-03-15T07:13:54Z)
• Measuring Fairness of Text Classifiers via Prediction Sensitivity [63.56554964580627]
  ACCUMULATED PREDICTION SENSITIVITY measures fairness in machine learning models based on the model's prediction sensitivity to perturbations in input features. We show that the metric can be theoretically linked with a specific notion of group fairness (statistical parity) and individual fairness.
  arXiv  Detail & Related papers  (2022-03-16T15:00:33Z)
• Normalise for Fairness: A Simple Normalisation Technique for Fairness in Regression Machine Learning Problems [46.93320580613236]
  We present a simple, yet effective method based on normalisation (FaiReg) for regression problems. We compare it with two standard methods for fairness, namely data balancing and adversarial training. The results show the superior performance of diminishing the effects of unfairness better than data balancing.
  arXiv  Detail & Related papers  (2022-02-02T12:26:25Z)
• Statistical inference for individual fairness [24.622418924551315]
  We focus on the problem of detecting violations of individual fairness in machine learning models. We develop a suite of inference tools for the adversarial cost function. We demonstrate the utility of our tools in a real-world case study.
  arXiv  Detail & Related papers  (2021-03-30T22:49:25Z)

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.