The \u201cReport on Biases in Artificial Intelligence\u201d presents the gender-based and racial forms of bias that can be observed in AI medical applications used in relation to three diseases: cardiovascular diseases, diabetes, and depression. Each type of bias is accompanied by its theoretical framework, examples, and a case study.<\/p>\n\n\n\n
In the section \u201cPolicy Implications,\u201d the report demonstrates how various types of bias in artificial intelligence impacts fundamental rights protected by the EU Charter, particularly the principles of human dignity, equality before the law, and non-discrimination, as well as the right to the integrity of the person, the right to healthcare, data protection, and the right to an effective legal protection.<\/p>\n\n\n\n
The report is part of the project \u201cAEQUITAS \u2013 Preparing Civil Society Organisations and the public healthcare sector to address gender and racial biases that may arise from the widespread use of Artificial Intelligence, with the aim of protecting and promoting fundamental rights.\u201d The project is implemented by a consortium of nine organisations: Center for the Study of Democracy (CSD) (coordinator), Diotima Centre<\/a> , <\/strong>Klinikum der Universit\u00e4t zu K\u00f6ln (UKK), Innovation Hive (INNOHIVE), Citizens in Power (CIP), Women\u2019s Issues Information Centre (WIIC), Lobby Europeo de Mujeres en Espa\u00f1a (LEM), TIA Formazione Internazionale APS, and Health Citizens European Institute. <\/p>\n","protected":false},"excerpt":{"rendered":" The report presents gender- and race-based forms of bias in Artificial Intelligence. <\/p>\n","protected":false},"author":2,"featured_media":41850,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[1145],"tags":[1967,1968,1966,1969],"class_list":["post-44995","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-2meletes-en","tag-artificial-intelligence","tag-bias","tag-gender-biases","tag-medical-applications"],"acf":[],"_links":{"self":[{"href":"https:\/\/diotima.org.gr\/en\/wp-json\/wp\/v2\/posts\/44995","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/diotima.org.gr\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/diotima.org.gr\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/diotima.org.gr\/en\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/diotima.org.gr\/en\/wp-json\/wp\/v2\/comments?post=44995"}],"version-history":[{"count":8,"href":"https:\/\/diotima.org.gr\/en\/wp-json\/wp\/v2\/posts\/44995\/revisions"}],"predecessor-version":[{"id":45009,"href":"https:\/\/diotima.org.gr\/en\/wp-json\/wp\/v2\/posts\/44995\/revisions\/45009"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/diotima.org.gr\/en\/wp-json\/wp\/v2\/media\/41850"}],"wp:attachment":[{"href":"https:\/\/diotima.org.gr\/en\/wp-json\/wp\/v2\/media?parent=44995"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/diotima.org.gr\/en\/wp-json\/wp\/v2\/categories?post=44995"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/diotima.org.gr\/en\/wp-json\/wp\/v2\/tags?post=44995"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
It is funded by the European Union under the CERV-2024-CHAR programme. Visit the project page here<\/a>. <\/em><\/p>\n\n\n\n