Fair Generalized Linear Mixed Models

• URL: http://arxiv.org/abs/2405.09273v2
• Date: Wed, 22 May 2024 06:08:03 GMT
• Title: Fair Generalized Linear Mixed Models
• Authors: Jan Pablo Burgard, João Vitor Pamplona,
• Abstract summary: Fairness in machine learning aims to ensure that biases in the data and model inaccuracies do not lead to discriminatory decisions. We present an algorithm that can handle both problems simultaneously.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: When using machine learning for automated prediction, it is important to account for fairness in the prediction. Fairness in machine learning aims to ensure that biases in the data and model inaccuracies do not lead to discriminatory decisions. E.g., predictions from fair machine learning models should not discriminate against sensitive variables such as sexual orientation and ethnicity. The training data often in obtained from social surveys. In social surveys, oftentimes the data collection process is a strata sampling, e.g. due to cost restrictions. In strata samples, the assumption of independence between the observation is not fulfilled. Hence, if the machine learning models do not account for the strata correlations, the results may be biased. Especially high is the bias in cases where the strata assignment is correlated to the variable of interest. We present in this paper an algorithm that can handle both problems simultaneously, and we demonstrate the impact of stratified sampling on the quality of fair machine learning predictions in a reproducible simulation study.

Related papers

• Fair Mixed Effects Support Vector Machine [0.0]
  Fairness in machine learning aims to mitigate biases present in the training data and model imperfections. This is achieved by preventing the model from making decisions based on sensitive characteristics like ethnicity or sexual orientation. We present a fair mixed effects support vector machine algorithm that can handle both problems simultaneously.
  arXiv  Detail & Related papers  (2024-05-10T12:25:06Z)
• Do We Really Even Need Data? [2.3749120526936465]
  Researchers increasingly use predictions from pre-trained algorithms as outcome variables. Standard tools for inference can misrepresent the association between independent variables and the outcome of interest when the true, unobserved outcome is replaced by a predicted value.
  arXiv  Detail & Related papers  (2024-01-14T23:19:21Z)
• 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)
• 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)
• Ecosystem-level Analysis of Deployed Machine Learning Reveals Homogeneous Outcomes [72.13373216644021]
  We study the societal impact of machine learning by considering the collection of models that are deployed in a given context. We find deployed machine learning is prone to systemic failure, meaning some users are exclusively misclassified by all models available. These examples demonstrate ecosystem-level analysis has unique strengths for characterizing the societal impact of machine learning.
  arXiv  Detail & Related papers  (2023-07-12T01:11:52Z)
• Provable Detection of Propagating Sampling Bias in Prediction Models [1.7709344190822935]
  We provide a theoretical analysis of how a specific form of data bias, differential sampling bias, propagates from the data stage to the prediction stage. Under reasonable assumptions, we quantify how the amount of bias in the model predictions varies as a function of the amount of differential sampling bias in the data. We demonstrate that the theoretical results hold in practice even when our assumptions are relaxed.
  arXiv  Detail & Related papers  (2023-02-13T23:39:35Z)
• 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)
• 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)
• Quality of Data in Machine Learning [3.9998518782208774]
  The study refutes the starting assumption and continues to state that in this case the significance in data lies in the quality of the data instead of the quantity of the data.
  arXiv  Detail & Related papers  (2021-12-17T09:22:46Z)
• A Note on High-Probability versus In-Expectation Guarantees of Generalization Bounds in Machine Learning [95.48744259567837]
  Statistical machine learning theory often tries to give generalization guarantees of machine learning models. Statements made about the performance of machine learning models have to take the sampling process into account. We show how one may transform one statement to another.
  arXiv  Detail & Related papers  (2020-10-06T09:41:35Z)
• Fairness in Semi-supervised Learning: Unlabeled Data Help to Reduce Discrimination [53.3082498402884]
  A growing specter in the rise of machine learning is whether the decisions made by machine learning models are fair. We present a framework of fair semi-supervised learning in the pre-processing phase, including pseudo labeling to predict labels for unlabeled data. A theoretical decomposition analysis of bias, variance and noise highlights the different sources of discrimination and the impact they have on fairness in semi-supervised learning.
  arXiv  Detail & Related papers  (2020-09-25T05:48:56Z)

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.