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The transformative impact of these new technologies warrants several considerations that demand the development of modern solutions through responsible artificial intelligence\u2014the incorporation of ethical principles into the creation and deployment of AI systems. Fairness is one such principle, ensuring that machine learning algorithms do not produce biased outcomes or discriminate against any group of the population with respect to sensitive attributes, such as race or gender. In this context, visualization techniques can help identify data imbalances and disparities in model performance across different demographic groups. However, there is a lack of guidance towards clear and effective representations that support entry-level users in fairness analysis, particularly when considering that the approaches to fairness visualization can vary significantly. In this regard, the goal of this work is to present a comprehensive analysis of current tools directed at visualizing and examining group fairness in machine learning, with a focus on both data and binary classification model outcomes. These visualization tools are reviewed and discussed, concluding with the proposition of a focused set of visualization guidelines directed towards improving the comprehensibility of fairness visualizations.<\/jats:p>","DOI":"10.1007\/s10462-025-11179-w","type":"journal-article","created":{"date-parts":[[2025,3,24]],"date-time":"2025-03-24T13:59:36Z","timestamp":1742824776000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Guidelines for designing visualization tools for group fairness analysis in binary classification"],"prefix":"10.1007","volume":"58","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9812-5781","authenticated-orcid":false,"given":"Ant\u00f3nio","family":"Cruz","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2471-5783","authenticated-orcid":false,"given":"Teresa","family":"Salazar","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8323-3445","authenticated-orcid":false,"given":"Manuel","family":"Carvalho","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4511-5763","authenticated-orcid":false,"given":"Catarina","family":"Ma\u00e7\u00e3s","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6308-6484","authenticated-orcid":false,"given":"Penousal","family":"Machado","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9278-8194","authenticated-orcid":false,"given":"Pedro Henriques","family":"Abreu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,3,24]]},"reference":[{"issue":"3","key":"11179_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3310231","volume":"11","author":"S Abiteboul","year":"2019","unstructured":"Abiteboul S, Stoyanovich J (2019) Transparency, fairness, data protection, neutrality: data management challenges in the face of new regulation. 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