{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,4]],"date-time":"2026-04-04T20:42:29Z","timestamp":1775335349280,"version":"3.50.1"},"reference-count":39,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2023,1,30]],"date-time":"2023-01-30T00:00:00Z","timestamp":1675036800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"SENAI CIMATEC and HP Brasil Ind\u00fastria e Com\u00e9rcio de Equipamentos Eletr\u00f4nicos Ltda. or Simpress Com\u00e9rcio, Loca\u00e7\u00e3o e Servi\u00e7os Ltda"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["BDCC"],"abstract":"<jats:p>The majority of current approaches for bias and fairness identification or mitigation in machine learning models are applications for a particular issue that fails to account for the connection between the application context and its associated sensitive attributes, which contributes to the recognition of consistent patterns in the application of bias and fairness metrics. This can be used to drive the development of future models, with the sensitive attribute acting as a connecting element to these metrics. Hence, this study aims to analyze patterns in several metrics for identifying bias and fairness, applying the gender-sensitive attribute as a case study, for three different areas of applications in machine learning models: computer vision, natural language processing, and recommendation systems. The gender attribute case study has been used in computer vision, natural language processing, and recommendation systems. The method entailed creating use cases for facial recognition in the FairFace dataset, message toxicity in the Jigsaw dataset, and movie recommendations in the MovieLens100K dataset, then developing models based on the VGG19, BERT, and Wide Deep architectures and evaluating them using the accuracy, precision, recall, and F1-score classification metrics, as well as assessing their outcomes using fourteen fairness metrics. Certain metrics disclosed bias and fairness, while others did not, revealing a consistent pattern for the same sensitive attribute across different application domains, and similarities for the statistical parity, PPR disparity, and error disparity metrics across domains, indicating fairness related to the studied sensitive attribute. Some attributes, on the other hand, did not follow this pattern. As a result, we conclude that the sensitive attribute may play a crucial role in defining the fairness metrics for a specific context.<\/jats:p>","DOI":"10.3390\/bdcc7010027","type":"journal-article","created":{"date-parts":[[2023,1,30]],"date-time":"2023-01-30T05:57:23Z","timestamp":1675058243000},"page":"27","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Context-Based Patterns in Machine Learning Bias and Fairness Metrics: A Sensitive Attributes-Based Approach"],"prefix":"10.3390","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2457-9064","authenticated-orcid":false,"given":"Tiago P.","family":"Pagano","sequence":"first","affiliation":[{"name":"Computational Modeling Department, SENAI CIMATEC University Center, Salvador 41650-010, BA, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2404-0643","authenticated-orcid":false,"given":"Rafael B.","family":"Loureiro","sequence":"additional","affiliation":[{"name":"Computational Modeling Department, SENAI CIMATEC University Center, Salvador 41650-010, BA, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5203-2408","authenticated-orcid":false,"given":"Fernanda V. N.","family":"Lisboa","sequence":"additional","affiliation":[{"name":"Computer Engineering, SENAI CIMATEC University Center, Salvador 41650-010, BA, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4613-9342","authenticated-orcid":false,"given":"Gustavo O. R.","family":"Cruz","sequence":"additional","affiliation":[{"name":"Computer Engineering, SENAI CIMATEC University Center, Salvador 41650-010, BA, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6405-1593","authenticated-orcid":false,"given":"Rodrigo M.","family":"Peixoto","sequence":"additional","affiliation":[{"name":"Software Development Department, Salvador 41650-010, BA, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7407-5908","authenticated-orcid":false,"given":"Guilherme A. de Sousa","family":"Guimar\u00e3es","sequence":"additional","affiliation":[{"name":"Software Development Department, Salvador 41650-010, BA, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3160-3571","authenticated-orcid":false,"given":"Ewerton L. S.","family":"Oliveira","sequence":"additional","affiliation":[{"name":"HP Inc. Brazil R&D, Porto Alegre 90619-900, RS, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6505-6636","authenticated-orcid":false,"given":"Ingrid","family":"Winkler","sequence":"additional","affiliation":[{"name":"Management and Industrial Technology Department, SENAI CIMATEC University Center, Salvador 41650-010, BA, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2219-0290","authenticated-orcid":false,"given":"Erick G. Sperandio","family":"Nascimento","sequence":"additional","affiliation":[{"name":"Computational Modeling Department, SENAI CIMATEC University Center, Salvador 41650-010, BA, Brazil"},{"name":"Surrey Institute for People-Centred AI, School of Computer Science and Electronic Engineering, Faculty of Engineering and Physical Sciences, University of Surrey, Guildford GU2 7XH, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1146\/annurev-statistics-042720-125902","article-title":"Algorithmic Fairness: Choices, Assumptions, and Definitions","volume":"8","author":"Mitchell","year":"2021","journal-title":"Annu. Rev. Stat. 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