Exploring Bias and Prediction Metrics to Characterise the Fairness of Machine Learning for Equity-Centered Public Health Decision-Making: A Narrative Review

• URL: http://arxiv.org/abs/2408.13295v2
• Date: Mon, 2 Sep 2024 17:00:05 GMT
• Title: Exploring Bias and Prediction Metrics to Characterise the Fairness of Machine Learning for Equity-Centered Public Health Decision-Making: A Narrative Review
• Authors: Shaina Raza, Arash Shaban-Nejad, Elham Dolatabadi, Hiroshi Mamiya,
• Abstract summary: There is a lack of comprehensive understanding of algorithmic bias, systematic errors in predicted population health outcomes, resulting from the public health application of Machine Learning. The review will help formalize the evaluation framework for ML on public health from an equity perspective.
• Score: 2.7757900645956943
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Background: The rapid advancement of Machine Learning (ML) represents novel opportunities to enhance public health research, surveillance, and decision-making. However, there is a lack of comprehensive understanding of algorithmic bias, systematic errors in predicted population health outcomes, resulting from the public health application of ML. The objective of this narrative review is to explore the types of bias generated by ML and quantitative metrics to assess these biases. Methods : We performed search on PubMed, MEDLINE, IEEE (Institute of Electrical and Electronics Engineers), ACM (Association for Computing Machinery) Digital Library, Science Direct, and Springer Nature. We used keywords to identify studies describing types of bias and metrics to measure these in the domain of ML and public and population health published in English between 2008 and 2023, inclusive. Results: A total of 72 articles met the inclusion criteria. Our review identified the commonly described types of bias and quantitative metrics to assess these biases from an equity perspective. Conclusion : The review will help formalize the evaluation framework for ML on public health from an equity perspective.

Related papers

• Systematic Review: Text Processing Algorithms in Machine Learning and Deep Learning for Mental Health Detection on Social Media [0.037693031068634524]
  This systematic review evaluates machine learning models for depression detection on social media. Significant biases impacting model reliability and generalizability were found. Only 23% of studies explicitly addressed linguistic nuances like negations, crucial for accurate sentiment analysis.
  arXiv  Detail & Related papers  (2024-10-21T17:05:50Z)
• Comprehensive Equity Index (CEI): Definition and Application to Bias Evaluation in Biometrics [47.762333925222926]
  We present a novel metric to quantify biased behaviors of machine learning models. We focus on and apply it to the operational evaluation of face recognition systems.
  arXiv  Detail & Related papers  (2024-09-03T14:19:38Z)
• A survey of recent methods for addressing AI fairness and bias in biomedicine [48.46929081146017]
  Artificial intelligence systems may perpetuate social inequities or demonstrate biases, such as those based on race or gender. We surveyed recent publications on different debiasing methods in the fields of biomedical natural language processing (NLP) or computer vision (CV) We performed a literature search on PubMed, ACM digital library, and IEEE Xplore of relevant articles published between January 2018 and December 2023 using multiple combinations of keywords. We reviewed other potential methods from the general domain that could be applied to biomedicine to address bias and improve fairness.
  arXiv  Detail & Related papers  (2024-02-13T06:38:46Z)
• Unmasking Bias in AI: A Systematic Review of Bias Detection and Mitigation Strategies in Electronic Health Record-based Models [6.300835344100545]
  Leveraging artificial intelligence in conjunction with electronic health records holds transformative potential to improve healthcare. Yet, addressing bias in AI, which risks worsening healthcare disparities, cannot be overlooked. This study reviews methods to detect and mitigate diverse forms of bias in AI models developed using EHR data.
  arXiv  Detail & Related papers  (2023-10-30T18:29:15Z)
• 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)
• Fairness in Machine Learning meets with Equity in Healthcare [6.842248432925292]
  This study proposes an artificial intelligence framework for identifying and mitigating biases in data and models. A case study is presented to demonstrate how systematic biases in data can lead to amplified biases in model predictions. Future research aims to test and validate the proposed ML framework in real-world clinical settings to evaluate its impact on promoting health equity.
  arXiv  Detail & Related papers  (2023-05-11T14:25:34Z)
• Connecting Fairness in Machine Learning with Public Health Equity [0.0]
  biases in data and model design can result in disparities for certain protected groups and amplify existing inequalities in healthcare. This study summarizes seminal literature on ML fairness and presents a framework for identifying and mitigating biases in the data and model. Case studies suggest how the framework can be used to prevent these biases and highlight the need for fair and equitable ML models in public health.
  arXiv  Detail & Related papers  (2023-04-08T10:21:49Z)
• Auditing Algorithmic Fairness in Machine Learning for Health with Severity-Based LOGAN [70.76142503046782]
  We propose supplementing machine learning-based (ML) healthcare tools for bias with SLOGAN, an automatic tool for capturing local biases in a clinical prediction task. LOGAN adapts an existing tool, LOcal Group biAs detectioN, by contextualizing group bias detection in patient illness severity and past medical history. On average, SLOGAN identifies larger fairness disparities in over 75% of patient groups than LOGAN while maintaining clustering quality.
  arXiv  Detail & Related papers  (2022-11-16T08:04:12Z)
• Social Biases in Automatic Evaluation Metrics for NLG [53.76118154594404]
  We propose an evaluation method based on Word Embeddings Association Test (WEAT) and Sentence Embeddings Association Test (SEAT) to quantify social biases in evaluation metrics. We construct gender-swapped meta-evaluation datasets to explore the potential impact of gender bias in image caption and text summarization tasks.
  arXiv  Detail & Related papers  (2022-10-17T08:55:26Z)
• Fair Machine Learning in Healthcare: A Review [90.22219142430146]
  We analyze the intersection of fairness in machine learning and healthcare disparities. We provide a critical review of the associated fairness metrics from a machine learning standpoint. We propose several new research directions that hold promise for developing ethical and equitable ML applications in healthcare.
  arXiv  Detail & Related papers  (2022-06-29T04:32:10Z)

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.