Analyzing Fairness of Classification Machine Learning Model with Structured Dataset

• URL: http://arxiv.org/abs/2412.09896v2
• Date: Tue, 17 Dec 2024 00:57:15 GMT
• Title: Analyzing Fairness of Classification Machine Learning Model with Structured Dataset
• Authors: Ahmed Rashed, Abdelkrim Kallich, Mohamed Eltayeb,
• Abstract summary: This study investigates the fairness of machine learning models applied to structured datasets in classification tasks. Three fairness libraries; Fairlearn by Microsoft, AIF360 by IBM, and the What If Tool by Google were employed. The research aims to assess the extent of bias in the ML models, compare the effectiveness of these libraries, and derive actionable insights for practitioners.
• Score: 1.0923877073891446
• License:
• Abstract: Machine learning (ML) algorithms have become integral to decision making in various domains, including healthcare, finance, education, and law enforcement. However, concerns about fairness and bias in these systems pose significant ethical and social challenges. This study investigates the fairness of ML models applied to structured datasets in classification tasks, highlighting the potential for biased predictions to perpetuate systemic inequalities. A publicly available dataset from Kaggle was selected for analysis, offering a realistic scenario for evaluating fairness in machine learning workflows. To assess and mitigate biases, three prominent fairness libraries; Fairlearn by Microsoft, AIF360 by IBM, and the What If Tool by Google were employed. These libraries provide robust frameworks for analyzing fairness, offering tools to evaluate metrics, visualize results, and implement bias mitigation strategies. The research aims to assess the extent of bias in the ML models, compare the effectiveness of these libraries, and derive actionable insights for practitioners. The findings reveal that each library has unique strengths and limitations in fairness evaluation and mitigation. By systematically comparing their capabilities, this study contributes to the growing field of ML fairness by providing practical guidance for integrating fairness tools into real world applications. These insights are intended to support the development of more equitable machine learning systems.

Related papers

• Analyzing Fairness of Computer Vision and Natural Language Processing Models [1.0923877073891446]
  Machine learning (ML) algorithms play a crucial role in decision making across diverse fields such as healthcare, finance, education, and law enforcement. Despite their widespread adoption, these systems raise ethical and social concerns due to potential biases and fairness issues. This study focuses on evaluating and improving the fairness of Computer Vision and Natural Language Processing (NLP) models applied to unstructured datasets.
  arXiv  Detail & Related papers  (2024-12-13T06:35:55Z)
• Causal Inference Tools for a Better Evaluation of Machine Learning [0.0]
  We introduce key statistical methods such as Ordinary Least Squares (OLS) regression, Analysis of Variance (ANOVA) and logistic regression. The document serves as a guide for researchers and practitioners, detailing how these techniques can provide deeper insights into model behavior, performance, and fairness.
  arXiv  Detail & Related papers  (2024-10-02T10:03:29Z)
• A Benchmark for Fairness-Aware Graph Learning [58.515305543487386]
  We present an extensive benchmark on ten representative fairness-aware graph learning methods. Our in-depth analysis reveals key insights into the strengths and limitations of existing methods.
  arXiv  Detail & Related papers  (2024-07-16T18:43:43Z)
• 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)
• FairLay-ML: Intuitive Remedies for Unfairness in Data-Driven Social-Critical Algorithms [13.649336187121095]
  This thesis explores whether open-sourced machine learning (ML) model explanation tools can allow a layman to visualize, understand, and suggest intuitive remedies to unfairness in ML-based decision-support systems. This thesis presents FairLay-ML, a proof-of-concept GUI integrating some of the most promising tools to provide intuitive explanations for unfair logic in ML models.
  arXiv  Detail & Related papers  (2023-07-11T06:05:06Z)
• Fairness meets Cross-Domain Learning: a new perspective on Models and Metrics [80.07271410743806]
  We study the relationship between cross-domain learning (CD) and model fairness. We introduce a benchmark on face and medical images spanning several demographic groups as well as classification and localization tasks. Our study covers 14 CD approaches alongside three state-of-the-art fairness algorithms and shows how the former can outperform the latter.
  arXiv  Detail & Related papers  (2023-03-25T09:34:05Z)
• Individual Fairness under Uncertainty [26.183244654397477]
  Algorithmic fairness is an established area in machine learning (ML) algorithms. We propose an individual fairness measure and a corresponding algorithm that deal with the challenges of uncertainty arising from censorship in class labels. We argue that this perspective represents a more realistic model of fairness research for real-world application deployment.
  arXiv  Detail & Related papers  (2023-02-16T01:07:58Z)
• A survey on datasets for fairness-aware machine learning [6.962333053044713]
  A large variety of fairness-aware machine learning solutions have been proposed. In this paper, we overview real-world datasets used for fairness-aware machine learning. For a deeper understanding of bias and fairness in the datasets, we investigate the interesting relationships using exploratory analysis.
  arXiv  Detail & Related papers  (2021-10-01T16:54:04Z)
• Leveraging Expert Consistency to Improve Algorithmic Decision Support [62.61153549123407]
  We explore the use of historical expert decisions as a rich source of information that can be combined with observed outcomes to narrow the construct gap. We propose an influence function-based methodology to estimate expert consistency indirectly when each case in the data is assessed by a single expert. Our empirical evaluation, using simulations in a clinical setting and real-world data from the child welfare domain, indicates that the proposed approach successfully narrows the construct gap.
  arXiv  Detail & Related papers  (2021-01-24T05:40:29Z)
• Causal Feature Selection for Algorithmic Fairness [61.767399505764736]
  We consider fairness in the integration component of data management. We propose an approach to identify a sub-collection of features that ensure the fairness of the dataset.
  arXiv  Detail & Related papers  (2020-06-10T20:20:10Z)

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.