Failing on Bias Mitigation: Investigating Why Predictive Models Struggle with Government Data

• URL: http://arxiv.org/abs/2601.17054v1
• Date: Wed, 21 Jan 2026 13:29:43 GMT
• Title: Failing on Bias Mitigation: Investigating Why Predictive Models Struggle with Government Data
• Authors: Hongbo Bo, Jingyu Hu, Debbie Watson, Weiru Liu,
• Abstract summary: The potential for bias and unfairness in AI-supporting government services raises ethical and legal concerns.<n>Using crime rate prediction with the Bristol City Council data as a case study, we examine how these issues persist.<n>Our findings reveal that bias mitigation approaches applied to government data are not always effective.
• Score: 3.2794063354054965
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The potential for bias and unfairness in AI-supporting government services raises ethical and legal concerns. Using crime rate prediction with the Bristol City Council data as a case study, we examine how these issues persist. Rather than auditing real-world deployed systems, our goal is to understand why widely adopted bias mitigation techniques often fail when applied to government data. Our findings reveal that bias mitigation approaches applied to government data are not always effective -- not because of flaws in model architecture or metric selection, but due to the inherent properties of the data itself. Through comparing a set of comprehensive models and fairness methods, our experiments consistently show that the mitigation efforts cannot overcome the embedded unfairness in the data -- further reinforcing that the origin of bias lies in the structure and history of government datasets. We then explore the reasons for the mitigation failures in predictive models on government data and highlight the potential sources of unfairness posed by data distribution shifts, the accumulation of historical bias, and delays in data release. We also discover the limitations of the blind spots in fairness analysis and bias mitigation methods when only targeting a single sensitive feature through a set of intersectional fairness experiments. Although this study is limited to one city, the findings are highly suggestive, which can contribute to an early warning that biases in government data may persist even with standard mitigation methods.

Related papers

• Identifying and Mitigating Social Bias Knowledge in Language Models [52.52955281662332]
  We propose a novel debiasing approach, Fairness Stamp (FAST), which enables fine-grained calibration of individual social biases.<n>FAST surpasses state-of-the-art baselines with superior debiasing performance.<n>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)
• AIM: Attributing, Interpreting, Mitigating Data Unfairness [40.351282126410545]
  Existing fair machine learning (FairML) research has predominantly focused on mitigating discriminative bias in the model prediction. We investigate a novel research problem: discovering samples that reflect biases/prejudices from the training data. We propose practical algorithms for measuring and countering sample bias.
  arXiv  Detail & Related papers  (2024-06-13T05:21:10Z)
• How to be fair? A study of label and selection bias [3.018638214344819]
  It is widely accepted that biased data leads to biased and potentially unfair models. Several measures for bias in data and model predictions have been proposed, as well as bias mitigation techniques. Despite the myriad of mitigation techniques developed in the past decade, it is still poorly understood under what circumstances which methods work.
  arXiv  Detail & Related papers  (2024-03-21T10:43:55Z)
• The Impact of Differential Feature Under-reporting on Algorithmic Fairness [86.275300739926]
  We present an analytically tractable model of differential feature under-reporting. We then use to characterize the impact of this kind of data bias on algorithmic fairness. Our results show that, in real world data settings, under-reporting typically leads to increasing disparities.
  arXiv  Detail & Related papers  (2024-01-16T19:16:22Z)
• 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)
• Membership Inference Attacks against Synthetic Data through Overfitting Detection [84.02632160692995]
  We argue for a realistic MIA setting that assumes the attacker has some knowledge of the underlying data distribution. We propose DOMIAS, a density-based MIA model that aims to infer membership by targeting local overfitting of the generative model.
  arXiv  Detail & Related papers  (2023-02-24T11:27:39Z)
• On Comparing Fair Classifiers under Data Bias [42.43344286660331]
  We study the effect of varying data biases on the accuracy and fairness of fair classifiers. Our experiments show how to integrate a measure of data bias risk in the existing fairness dashboards for real-world deployments.
  arXiv  Detail & Related papers  (2023-02-12T13:04:46Z)
• Rethinking Bias Mitigation: Fairer Architectures Make for Fairer Face Recognition [107.58227666024791]
  Face recognition systems are widely deployed in safety-critical applications, including law enforcement. They exhibit bias across a range of socio-demographic dimensions, such as gender and race. Previous works on bias mitigation largely focused on pre-processing the training data.
  arXiv  Detail & Related papers  (2022-10-18T15:46:05Z)
• D-BIAS: A Causality-Based Human-in-the-Loop System for Tackling Algorithmic Bias [57.87117733071416]
  We propose D-BIAS, a visual interactive tool that embodies human-in-the-loop AI approach for auditing and mitigating social biases. A user can detect the presence of bias against a group by identifying unfair causal relationships in the causal network. For each interaction, say weakening/deleting a biased causal edge, the system uses a novel method to simulate a new (debiased) dataset.
  arXiv  Detail & Related papers  (2022-08-10T03:41:48Z)
• Understanding Unfairness in Fraud Detection through Model and Data Bias Interactions [4.159343412286401]
  We argue that algorithmic unfairness stems from interactions between models and biases in the data. We study a set of hypotheses regarding the fairness-accuracy trade-offs that fairness-blind ML algorithms exhibit under different data bias settings.
  arXiv  Detail & Related papers  (2022-07-13T15:18:30Z)
• Prisoners of Their Own Devices: How Models Induce Data Bias in Performative Prediction [4.874780144224057]
  A biased model can make decisions that disproportionately harm certain groups in society. Much work has been devoted to measuring unfairness in static ML environments, but not in dynamic, performative prediction ones. We propose a taxonomy to characterize bias in the data, and study cases where it is shaped by model behaviour.
  arXiv  Detail & Related papers  (2022-06-27T10:56:04Z)

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.