{"id":4183,"date":"2024-04-27T11:05:48","date_gmt":"2024-04-27T16:05:48","guid":{"rendered":"https:\/\/cirics.uqo.ca\/alan-davoust\/"},"modified":"2025-02-07T10:04:09","modified_gmt":"2025-02-07T15:04:09","slug":"alan-davoust","status":"publish","type":"page","link":"https:\/\/cirics.uqo.ca\/en\/alan-davoust\/","title":{"rendered":"Alan Davoust"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-page\" data-elementor-id=\"4183\" class=\"elementor elementor-4183 elementor-2032\">\n\t\t\t\t<div class=\"elementor-element elementor-element-e09216a e-flex e-con-boxed e-con e-parent\" data-id=\"e09216a\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-9386cfe elementor-hidden-desktop elementor-hidden-tablet elementor-hidden-mobile e-flex e-con-boxed e-con e-parent\" data-id=\"9386cfe\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-ea9c07d elementor-widget elementor-widget-heading\" data-id=\"ea9c07d\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Welcome<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-70e53c3 elementor-widget elementor-widget-text-editor\" data-id=\"70e53c3\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>If needed, this section can contain some or all of the following:<\/p>\n<ul>\n<li>A large, engaging image of the university, department, or an abstract representation of the academic field can set a professional and inspiring tone.<\/li>\n<li>A brief welcome message or introduction that explains what visitors will find on the page. This could be a short paragraph detailing the purpose of the page, such as highlighting the academic and research achievements of the faculty.<\/li>\n<li>Key facts, achievements or statistics about the professor or department. For instance, number of published papers, years of experience, key projects, or awards won.<\/li>\n<li>Interactive timeline that highlights major milestones, such as significant publications, awards, and other achievements.<\/li>\n<li>A short video where the professor introduces themselves and talk about their work and interests providing a personal touch, and making the page more engaging and approachable.<\/li>\n<\/ul>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-9b8f585 e-flex e-con-boxed e-con e-parent\" data-id=\"9b8f585\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t<div class=\"elementor-element elementor-element-99badd5 e-con-full my-profs-page-image-card e-flex e-con e-child\" data-id=\"99badd5\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-d8d58ce eael-team-align-centered elementor-widget elementor-widget-eael-team-member\" data-id=\"d8d58ce\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"eael-team-member.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\n\n\t<div id=\"eael-team-member-d8d58ce\" class=\"eael-team-item eael-team-members-simple team-avatar-rounded\">\n\t\t<div class=\"eael-team-item-inner\">\n\t\t\t<div class=\"eael-team-image\">\n\t\t\t\t<figure>\n\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" src=\"https:\/\/cirics.uqo.ca\/wp-content\/uploads\/2024\/04\/Alan_Davoust-300x300-1.webp\" alt=\"\">\n\t\t\t\t\t\t\t\t\t<\/figure>\n\t\t\t\t\n\t\t\t\t\n\t\t\t<\/div>\n\n\t\t\t<div class=\"eael-team-content\">\n\t\t\t\t<h2 class=\"eael-team-member-name\">Alan Davoust<\/h2><h3 class=\"eael-team-member-position\"><span>Professor<\/span><br> Universit\u00e9 du Qu\u00e9bec en Outaouais (UQO)<br> Computer Science and Engineering Department<\/h3>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<ul 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41.4z\"><\/path><\/svg>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/a>\n\t\t\t\t\t\t\t\t\t<\/li>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<li class=\"eael-team-member-social-link\">\n\t\t\t\t\t\t\t\t\t\t<a href=\"https:\/\/www.linkedin.com\/\" target=\"_blank\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fab-linkedin\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 32H31.9C14.3 32 0 46.5 0 64.3v383.4C0 465.5 14.3 480 31.9 480H416c17.6 0 32-14.5 32-32.3V64.3c0-17.8-14.4-32.3-32-32.3zM135.4 416H69V202.2h66.5V416zm-33.2-243c-21.3 0-38.5-17.3-38.5-38.5S80.9 96 102.2 96c21.2 0 38.5 17.3 38.5 38.5 0 21.3-17.2 38.5-38.5 38.5zm282.1 243h-66.4V312c0-24.8-.5-56.7-34.5-56.7-34.6 0-39.9 27-39.9 54.9V416h-66.4V202.2h63.7v29.2h.9c8.9-16.8 30.6-34.5 62.9-34.5 67.2 0 79.7 44.3 79.7 101.9V416z\"><\/path><\/svg>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/a>\n\t\t\t\t\t\t\t\t\t<\/li>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<li class=\"eael-team-member-social-link\">\n\t\t\t\t\t\t\t\t\t\t<a href=\"https:\/\/www.researchgate.net\/\" target=\"_blank\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fab-researchgate\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M0 32v448h448V32H0zm262.2 334.4c-6.6 3-33.2 6-50-14.2-9.2-10.6-25.3-33.3-42.2-63.6-8.9 0-14.7 0-21.4-.6v46.4c0 23.5 6 21.2 25.8 23.9v8.1c-6.9-.3-23.1-.8-35.6-.8-13.1 0-26.1.6-33.6.8v-8.1c15.5-2.9 22-1.3 22-23.9V225c0-22.6-6.4-21-22-23.9V193c25.8 1 53.1-.6 70.9-.6 31.7 0 55.9 14.4 55.9 45.6 0 21.1-16.7 42.2-39.2 47.5 13.6 24.2 30 45.6 42.2 58.9 7.2 7.8 17.2 14.7 27.2 14.7v7.3zm22.9-135c-23.3 0-32.2-15.7-32.2-32.2V167c0-12.2 8.8-30.4 34-30.4s30.4 17.9 30.4 17.9l-10.7 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421.8z\"><\/path><\/svg>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/a>\n\t\t\t\t\t\t\t\t\t<\/li>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<li class=\"eael-team-member-social-link\">\n\t\t\t\t\t\t\t\t\t\t<a href=\"https:\/\/www.facebook.com\/\" target=\"_blank\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fab-facebook\" viewBox=\"0 0 512 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M504 256C504 119 393 8 256 8S8 119 8 256c0 123.78 90.69 226.38 209.25 245V327.69h-63V256h63v-54.64c0-62.15 37-96.48 93.67-96.48 27.14 0 55.52 4.84 55.52 4.84v61h-31.28c-30.8 0-40.41 19.12-40.41 38.73V256h68.78l-11 71.69h-57.78V501C413.31 482.38 504 379.78 504 256z\"><\/path><\/svg>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/a>\n\t\t\t\t\t\t\t\t\t<\/li>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<li 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9s-102.7 2.6-132.1-9c-19.6-7.8-34.7-22.9-42.6-42.6-11.7-29.5-9-99.5-9-132.1s-2.6-102.7 9-132.1c7.8-19.6 22.9-34.7 42.6-42.6 29.5-11.7 99.5-9 132.1-9s102.7-2.6 132.1 9c19.6 7.8 34.7 22.9 42.6 42.6 11.7 29.5 9 99.5 9 132.1s2.7 102.7-9 132.1z\"><\/path><\/svg>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/a>\n\t\t\t\t\t\t\t\t\t<\/li>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<li class=\"eael-team-member-social-link\">\n\t\t\t\t\t\t\t\t\t\t<a href=\"mailto:alan.davoust@uqo.ca\" target=\"_blank\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-envelope\" viewBox=\"0 0 512 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M502.3 190.8c3.9-3.1 9.7-.2 9.7 4.7V400c0 26.5-21.5 48-48 48H48c-26.5 0-48-21.5-48-48V195.6c0-5 5.7-7.8 9.7-4.7 22.4 17.4 52.1 39.5 154.1 113.6 21.1 15.4 56.7 47.8 92.2 47.6 35.7.3 72-32.8 92.3-47.6 102-74.1 131.6-96.3 154-113.7zM256 320c23.2.4 56.6-29.2 73.4-41.4 132.7-96.3 142.8-104.7 173.4-128.7 5.8-4.5 9.2-11.5 9.2-18.9v-19c0-26.5-21.5-48-48-48H48C21.5 64 0 85.5 0 112v19c0 7.4 3.4 14.3 9.2 18.9 30.6 23.9 40.7 32.4 173.4 128.7 16.8 12.2 50.2 41.8 73.4 41.4z\"><\/path><\/svg>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/a>\n\t\t\t\t\t\t\t\t\t<\/li>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/ul>\n\t\t\t\t\t\t\t\t\t\t<p class=\"eael-team-text\">Since 2015, Alan Davoust has held a Ph. in Computer Engineering from Carleton University in Ottawa. A regular professor at UQO since December 2018, and holder of NSERC Discovery funding and principal investigator of an FRQSC-funded project on Disinformation in Quebec, he brings to our team his expertise on issues related to artificial intelligence, seen from a socio-technical systems perspective.<\/p>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-aaa41d5 e-con-full my-profs-page-publ-card e-flex e-con e-child\" data-id=\"aaa41d5\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-a474f56 elementor-widget elementor-widget-heading\" data-id=\"a474f56\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Productions included in the research:<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-ce5ef54 elementor-widget elementor-widget-text-editor\" data-id=\"ce5ef54\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p style=\"text-align: left;\"><strong>AUT<\/strong> (Other), BRE (Patent), <strong>CAC<\/strong> (Refereed publications in conference proceedings), <strong>CNA<\/strong> (Non-refereed paper), <strong>COC<\/strong> (Contribution to a collective work), <strong>COF<\/strong> (Refereed paper), <strong>CRE<\/strong>, <strong>GRO<\/strong>, <strong>LIV<\/strong> (Book), <strong>RAC<\/strong> (Refereed journal), <strong>RAP<\/strong> (Research report), <strong>RSC<\/strong> (Non-refereed journal).<\/p>\n<p style=\"text-align: center;\"><span style=\"text-shadow: 1px 1px 2px gray;\">Year: 1975 to 2024<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-107306a elementor-align-center my-prof-publ-but elementor-widget elementor-widget-button\" data-id=\"107306a\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm\" href=\"https:\/\/cirics.uqo.ca\/publications\/?tsr=&#038;auth=171\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t<span class=\"elementor-button-icon\">\n\t\t\t\t<svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-book-open\" viewBox=\"0 0 576 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M542.22 32.05c-54.8 3.11-163.72 14.43-230.96 55.59-4.64 2.84-7.27 7.89-7.27 13.17v363.87c0 11.55 12.63 18.85 23.28 13.49 69.18-34.82 169.23-44.32 218.7-46.92 16.89-.89 30.02-14.43 30.02-30.66V62.75c.01-17.71-15.35-31.74-33.77-30.7zM264.73 87.64C197.5 46.48 88.58 35.17 33.78 32.05 15.36 31.01 0 45.04 0 62.75V400.6c0 16.24 13.13 29.78 30.02 30.66 49.49 2.6 149.59 12.11 218.77 46.95 10.62 5.35 23.21-1.94 23.21-13.46V100.63c0-5.29-2.62-10.14-7.27-12.99z\"><\/path><\/svg>\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t<span class=\"elementor-button-text\">All publications<\/span>\n\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-eb6a7a7 elementor-widget elementor-widget-heading\" data-id=\"eb6a7a7\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Selected publications<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-29ef351 elementor-widget elementor-widget-shortcode\" data-id=\"29ef351\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"shortcode.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-shortcode\"><div class=\"teachpress_pub_list\"><form name=\"tppublistform\" method=\"get\"><a name=\"tppubs\" id=\"tppubs\"><\/a><\/form><table class=\"teachpress_publication_list\"><tr>\r\n                    <td>\r\n                        <h3 class=\"tp_h3\" id=\"tp_h3_2026\">2026<\/h3>\r\n                    <\/td>\r\n                <\/tr><tr class=\"tp_publication tp_publication_article\"><td class=\"tp_pub_info\"><p class=\"tp_pub_author\"> Damadi, M. S.;  Davoust, A.<\/p><p class=\"tp_pub_title\"><a class=\"tp_title_link\" onclick=\"teachpress_pub_showhide('549','tp_abstract')\" style=\"cursor:pointer;\">Fairness in social machines: a systematic review<\/a> <span class=\"tp_pub_type tp_  article\">Journal Article<\/span> <\/p><p class=\"tp_pub_additional\"><span class=\"tp_pub_additional_in\">In: <\/span><span class=\"tp_pub_additional_journal\">Journal of Information, Communication and Ethics in Society, <\/span><span class=\"tp_pub_additional_pages\">pp. 1\u201340, <\/span><span class=\"tp_pub_additional_year\">2026<\/span>, <span class=\"tp_pub_additional_issn\">ISSN: 1477996X (ISSN)<\/span>.<\/p><p class=\"tp_pub_menu\"><span class=\"tp_abstract_link\"><a id=\"tp_abstract_sh_549\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('549','tp_abstract')\" title=\"Show abstract\" style=\"cursor:pointer;\">Abstract<\/a><\/span> | <span class=\"tp_bibtex_link\"><a id=\"tp_bibtex_sh_549\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('549','tp_bibtex')\" title=\"Show BibTeX entry\" style=\"cursor:pointer;\">BibTeX<\/a><\/span>  |  <span class=\"tp_pub_links_label\">Links: <\/span><a class=\"tp_pub_link\" href=\"https:\/\/www.scopus.com\/inward\/record.uri?eid=2-s2.0-105030531101&amp;doi=10.1108%2FJICES-01-2025-0002&amp;partnerID=40&amp;md5=63e2f87ee3852ffe2d49c514e38cba1c\" title=\"https:\/\/www.scopus.com\/inward\/record.uri?eid=2-s2.0-105030531101&amp;doi=10.1108%2FJICES-01-2025-0002&amp;partnerID=40&amp;md5=63e2f87ee3852ffe2d49c514e38cba1c\" target=\"_blank\"><i class=\"fas fa-globe\"><\/i><\/a><a class=\"tp_pub_link\" href=\"https:\/\/dx.doi.org\/10.1108\/JICES-01-2025-0002\" title=\"Follow DOI:10.1108\/JICES-01-2025-0002\" target=\"_blank\"><i class=\"ai ai-doi\"><\/i><\/a><\/span><\/p><div class=\"tp_bibtex\" id=\"tp_bibtex_549\" style=\"display:none;\"><div class=\"tp_bibtex_entry\"><pre>@article{damadi_fairness_2026,<br \/>\r\ntitle = {Fairness in social machines: a systematic review},<br \/>\r\nauthor = {M. S. Damadi and A. Davoust},<br \/>\r\nurl = {https:\/\/www.scopus.com\/inward\/record.uri?eid=2-s2.0-105030531101&doi=10.1108%2FJICES-01-2025-0002&partnerID=40&md5=63e2f87ee3852ffe2d49c514e38cba1c},<br \/>\r\ndoi = {10.1108\/JICES-01-2025-0002},<br \/>\r\nissn = {1477996X (ISSN)},<br \/>\r\nyear  = {2026},<br \/>\r\ndate = {2026-01-01},<br \/>\r\njournal = {Journal of Information, Communication and Ethics in Society},<br \/>\r\npages = {1\u201340},<br \/>\r\nabstract = {Purpose \u2013 The purpose of the paper is to provide a systematic review of biases in social machines to better understand the general problem of fairness in these systems. It aims to identify and categorize phenomena described as biases toward specific demographic groups, frame them normatively as harmful and relate them to established fairness concepts originally defined for algorithmic systems. Design\/methodology\/approach \u2013 The phenomenon of algorithmic bias refers to systematic biases against identifiable demographic groups that occur in automated decisions systems. Such biases have mostly been studied in the context of black-box decision systems built using machine learning (ML). However, similar problems have also been reported in complex socio-technical systems such as Wikipedia and Airbnb, known more generally as social machines, where the observed biases cannot necessarily be attributed to specific automated decision systems. Instead, the biases may emerge as a result of complex processes involving numerous users and a computational infrastructure. To gain a better understanding of fairness in social machines, the authors select a representative sample of social machines from six distinct categories, and systematically review the literature reporting biases in these systems, covering 196 papers. The authors classify the reported bias phenomena, identify the affected demographic groups and relate the phenomena to established notions of harm from algorithmic fairness research. Finally, the authors identify the normative expectations of fairness associated with the different problems and discuss the applicability of existing criteria proposed for ML-driven decision systems. The analysis highlights the conceptual similarity of bias phenomena between algorithmic systems and social machines, allowing for a shared vocabulary to describe and compare phenomena across a broad class of systems. Findings \u2013 The paper identifies two key biases in social machines: representational harm, from underrepresentation or biased portrayal of disadvantaged groups, and allocative harm, from unfair decision processes, measurable via metrics like demographic parity. Gender bias is prevalent and easier to detect due to explicit markers, offering insights for identifying other biases. Unique biases arise from user categorizations, creating unintended discrimination linked to protected characteristics. These biases result from complex user interactions, not isolated algorithms. Addressing them requires redesigning social machines, focusing on computational infrastructure and interaction norms, such as visibility settings, to mitigate harmful outcomes. Originality\/value \u2013 The paper\u2019s originality lies in its systematic review of biases in social machines, offering a novel perspective on fairness in these systems. Unlike prior studies focusing solely on algorithmic fairness, this work examines the broader socio-technical interactions within social machines, identifying biases that emerge from user interactions and design choices. By linking these biases to established fairness concepts like demographic parity and representational harm, the paper bridges the gap between algorithmic fairness and social dynamics. \u00a9 2025 Emerald Publishing Limited},<br \/>\r\nkeywords = {},<br \/>\r\npubstate = {published},<br \/>\r\ntppubtype = {article}<br \/>\r\n}<br \/>\r\n<\/pre><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('549','tp_bibtex')\">Close<\/a><\/p><\/div><div class=\"tp_abstract\" id=\"tp_abstract_549\" style=\"display:none;\"><div class=\"tp_abstract_entry\">Purpose \u2013 The purpose of the paper is to provide a systematic review of biases in social machines to better understand the general problem of fairness in these systems. It aims to identify and categorize phenomena described as biases toward specific demographic groups, frame them normatively as harmful and relate them to established fairness concepts originally defined for algorithmic systems. Design\/methodology\/approach \u2013 The phenomenon of algorithmic bias refers to systematic biases against identifiable demographic groups that occur in automated decisions systems. Such biases have mostly been studied in the context of black-box decision systems built using machine learning (ML). However, similar problems have also been reported in complex socio-technical systems such as Wikipedia and Airbnb, known more generally as social machines, where the observed biases cannot necessarily be attributed to specific automated decision systems. Instead, the biases may emerge as a result of complex processes involving numerous users and a computational infrastructure. To gain a better understanding of fairness in social machines, the authors select a representative sample of social machines from six distinct categories, and systematically review the literature reporting biases in these systems, covering 196 papers. The authors classify the reported bias phenomena, identify the affected demographic groups and relate the phenomena to established notions of harm from algorithmic fairness research. Finally, the authors identify the normative expectations of fairness associated with the different problems and discuss the applicability of existing criteria proposed for ML-driven decision systems. The analysis highlights the conceptual similarity of bias phenomena between algorithmic systems and social machines, allowing for a shared vocabulary to describe and compare phenomena across a broad class of systems. Findings \u2013 The paper identifies two key biases in social machines: representational harm, from underrepresentation or biased portrayal of disadvantaged groups, and allocative harm, from unfair decision processes, measurable via metrics like demographic parity. Gender bias is prevalent and easier to detect due to explicit markers, offering insights for identifying other biases. Unique biases arise from user categorizations, creating unintended discrimination linked to protected characteristics. These biases result from complex user interactions, not isolated algorithms. Addressing them requires redesigning social machines, focusing on computational infrastructure and interaction norms, such as visibility settings, to mitigate harmful outcomes. Originality\/value \u2013 The paper\u2019s originality lies in its systematic review of biases in social machines, offering a novel perspective on fairness in these systems. Unlike prior studies focusing solely on algorithmic fairness, this work examines the broader socio-technical interactions within social machines, identifying biases that emerge from user interactions and design choices. By linking these biases to established fairness concepts like demographic parity and representational harm, the paper bridges the gap between algorithmic fairness and social dynamics. \u00a9 2025 Emerald Publishing Limited<\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('549','tp_abstract')\">Close<\/a><\/p><\/div><\/td><\/tr><tr>\r\n                    <td>\r\n                        <h3 class=\"tp_h3\" id=\"tp_h3_2025\">2025<\/h3>\r\n                    <\/td>\r\n                <\/tr><tr class=\"tp_publication tp_publication_article\"><td class=\"tp_pub_info\"><p class=\"tp_pub_author\"> Souza, J. V.;  Amamou, H.;  Chen, R.;  Salari, E.;  Gubelmann, R.;  Niklaus, C.;  Serpa, T.;  Lima, M. M. F.;  Pinto, P. T.;  Kshirsagar, S.;  Davoust, A.;  Handschuh, S.;  Avila, A. R.<\/p><p class=\"tp_pub_title\"><a class=\"tp_title_link\" onclick=\"teachpress_pub_showhide('551','tp_abstract')\" style=\"cursor:pointer;\">Cross-Lingual Keyword Extraction for Pesticide Terminology in Brazilian Portuguese and English<\/a> <span class=\"tp_pub_type tp_  article\">Journal Article<\/span> <\/p><p class=\"tp_pub_additional\"><span class=\"tp_pub_additional_in\">In: <\/span><span class=\"tp_pub_additional_journal\">Journal of the Brazilian Computer Society, <\/span><span class=\"tp_pub_additional_volume\">vol. 31, <\/span><span class=\"tp_pub_additional_number\">no. 1, <\/span><span class=\"tp_pub_additional_pages\">pp. 973\u2013990, <\/span><span class=\"tp_pub_additional_year\">2025<\/span>, <span class=\"tp_pub_additional_issn\">ISSN: 01046500 (ISSN)<\/span>.<\/p><p class=\"tp_pub_menu\"><span class=\"tp_abstract_link\"><a id=\"tp_abstract_sh_551\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('551','tp_abstract')\" title=\"Show abstract\" style=\"cursor:pointer;\">Abstract<\/a><\/span> | <span class=\"tp_bibtex_link\"><a id=\"tp_bibtex_sh_551\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('551','tp_bibtex')\" title=\"Show BibTeX entry\" style=\"cursor:pointer;\">BibTeX<\/a><\/span>  |  <span class=\"tp_pub_links_label\">Links: <\/span><a class=\"tp_pub_link\" href=\"https:\/\/www.scopus.com\/inward\/record.uri?eid=2-s2.0-105019700300&amp;doi=10.5753%2Fjbcs.2025.5815&amp;partnerID=40&amp;md5=85ee75baf4550666a307310cd04d1c83\" title=\"https:\/\/www.scopus.com\/inward\/record.uri?eid=2-s2.0-105019700300&amp;doi=10.5753%2Fjbcs.2025.5815&amp;partnerID=40&amp;md5=85ee75baf4550666a307310cd04d1c83\" target=\"_blank\"><i class=\"fas fa-globe\"><\/i><\/a><a class=\"tp_pub_link\" href=\"https:\/\/dx.doi.org\/10.5753\/jbcs.2025.5815\" title=\"Follow DOI:10.5753\/jbcs.2025.5815\" target=\"_blank\"><i class=\"ai ai-doi\"><\/i><\/a><\/span><\/p><div class=\"tp_bibtex\" id=\"tp_bibtex_551\" style=\"display:none;\"><div class=\"tp_bibtex_entry\"><pre>@article{de_souza_cross-lingual_2025,<br \/>\r\ntitle = {Cross-Lingual Keyword Extraction for Pesticide Terminology in Brazilian Portuguese and English},<br \/>\r\nauthor = {J. V. Souza and H. Amamou and R. Chen and E. Salari and R. Gubelmann and C. Niklaus and T. Serpa and M. M. F. Lima and P. T. Pinto and S. Kshirsagar and A. Davoust and S. Handschuh and A. R. Avila},<br \/>\r\nurl = {https:\/\/www.scopus.com\/inward\/record.uri?eid=2-s2.0-105019700300&doi=10.5753%2Fjbcs.2025.5815&partnerID=40&md5=85ee75baf4550666a307310cd04d1c83},<br \/>\r\ndoi = {10.5753\/jbcs.2025.5815},<br \/>\r\nissn = {01046500 (ISSN)},<br \/>\r\nyear  = {2025},<br \/>\r\ndate = {2025-01-01},<br \/>\r\njournal = {Journal of the Brazilian Computer Society},<br \/>\r\nvolume = {31},<br \/>\r\nnumber = {1},<br \/>\r\npages = {973\u2013990},<br \/>\r\nabstract = {Agriculture plays a crucial role in Brazil\u2019s economy. As the country intensifies its activities in the sector, the use of pesticides also increases. Hence, the risks associated with pesticide-laden food consumption have become a concern for chemistry researchers. An issue affecting regulatory standardization of pesticides in Brazil is the difficulty in translating pesticide names, particularly from English. For example, the word malathion can be translated from English to Portuguese as malatiom or malati\u00e3o, resulting in inconsistent labeling. This issue extends to the broader problem of translating highly technical terms between languages, in particular for low-resource languages. In this work, we investigate terminological variation in the chemistry of organophosphorus pesticides. Our goal is to study strategies for domain-specific multilingual keyword extraction. To that end, two corpora were built based on pesticide-related scientific documents in Brazilian Portuguese and English, which led to a total of 84 and 210 texts, respectively, representing the low-and high-resource languages in this study. We then assessed 6 methods for keyword extraction: Simple Maths, TF-IDF, YAKE, TextRank, MultipartiteRank, and KeyBERT. We relied on a multilingual contextual BERT embedding to retrieve corresponding pesticide names in the target language. Finetuning was also explored to improve the multilingual representation further. Moreover, we evaluated the use of large language models (LLMs) combined with the recent retrieval-augmented generation (RAG) framework. As a result, we found that the contextual approach, combined with fine-tuning, provided the best results, contributing to enhancing Pesticide Terminology Extraction in a multilingual scenario. \u00a9 2025, Brazilian Computing Society. All rights reserved.},<br \/>\r\nkeywords = {},<br \/>\r\npubstate = {published},<br \/>\r\ntppubtype = {article}<br \/>\r\n}<br \/>\r\n<\/pre><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('551','tp_bibtex')\">Close<\/a><\/p><\/div><div class=\"tp_abstract\" id=\"tp_abstract_551\" style=\"display:none;\"><div class=\"tp_abstract_entry\">Agriculture plays a crucial role in Brazil\u2019s economy. As the country intensifies its activities in the sector, the use of pesticides also increases. Hence, the risks associated with pesticide-laden food consumption have become a concern for chemistry researchers. An issue affecting regulatory standardization of pesticides in Brazil is the difficulty in translating pesticide names, particularly from English. For example, the word malathion can be translated from English to Portuguese as malatiom or malati\u00e3o, resulting in inconsistent labeling. This issue extends to the broader problem of translating highly technical terms between languages, in particular for low-resource languages. In this work, we investigate terminological variation in the chemistry of organophosphorus pesticides. Our goal is to study strategies for domain-specific multilingual keyword extraction. To that end, two corpora were built based on pesticide-related scientific documents in Brazilian Portuguese and English, which led to a total of 84 and 210 texts, respectively, representing the low-and high-resource languages in this study. We then assessed 6 methods for keyword extraction: Simple Maths, TF-IDF, YAKE, TextRank, MultipartiteRank, and KeyBERT. We relied on a multilingual contextual BERT embedding to retrieve corresponding pesticide names in the target language. Finetuning was also explored to improve the multilingual representation further. Moreover, we evaluated the use of large language models (LLMs) combined with the recent retrieval-augmented generation (RAG) framework. As a result, we found that the contextual approach, combined with fine-tuning, provided the best results, contributing to enhancing Pesticide Terminology Extraction in a multilingual scenario. \u00a9 2025, Brazilian Computing Society. All rights reserved.<\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('551','tp_abstract')\">Close<\/a><\/p><\/div><\/td><\/tr><tr class=\"tp_publication tp_publication_inproceedings\"><td class=\"tp_pub_info\"><p class=\"tp_pub_author\"> Ngouanfouo, C.;  Davoust, A.<\/p><p class=\"tp_pub_title\"><a class=\"tp_title_link\" onclick=\"teachpress_pub_showhide('557','tp_abstract')\" style=\"cursor:pointer;\">Detecting Machine-Generated Text using Grammatical Features<\/a> <span class=\"tp_pub_type tp_  inproceedings\">Proceedings Article<\/span> <\/p><p class=\"tp_pub_additional\"><span class=\"tp_pub_additional_in\">In: <\/span><span class=\"tp_pub_additional_booktitle\">Proc. Int. Conf. Tools Artif. Intell. ICTAI, <\/span><span class=\"tp_pub_additional_pages\">pp. 843\u2013848, <\/span><span class=\"tp_pub_additional_publisher\">IEEE Computer Society, <\/span><span class=\"tp_pub_additional_year\">2025<\/span>, <span class=\"tp_pub_additional_isbn\">ISBN: 10823409 (ISSN); 979-833154919-0 (ISBN)<\/span>.<\/p><p class=\"tp_pub_menu\"><span class=\"tp_abstract_link\"><a id=\"tp_abstract_sh_557\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('557','tp_abstract')\" title=\"Show abstract\" style=\"cursor:pointer;\">Abstract<\/a><\/span> | <span class=\"tp_bibtex_link\"><a id=\"tp_bibtex_sh_557\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('557','tp_bibtex')\" title=\"Show BibTeX entry\" style=\"cursor:pointer;\">BibTeX<\/a><\/span>  |  <span class=\"tp_pub_links_label\">Links: <\/span><a class=\"tp_pub_link\" href=\"https:\/\/www.scopus.com\/inward\/record.uri?eid=2-s2.0-105031903675&amp;doi=10.1109%2FICTAI66417.2025.00123&amp;partnerID=40&amp;md5=5783b8797a3425f9dfa737343ee757d2\" title=\"https:\/\/www.scopus.com\/inward\/record.uri?eid=2-s2.0-105031903675&amp;doi=10.1109%2FICTAI66417.2025.00123&amp;partnerID=40&amp;md5=5783b8797a3425f9dfa737343ee757d2\" target=\"_blank\"><i class=\"fas fa-globe\"><\/i><\/a><a class=\"tp_pub_link\" href=\"https:\/\/dx.doi.org\/10.1109\/ICTAI66417.2025.00123\" title=\"Follow DOI:10.1109\/ICTAI66417.2025.00123\" target=\"_blank\"><i class=\"ai ai-doi\"><\/i><\/a><\/span><\/p><div class=\"tp_bibtex\" id=\"tp_bibtex_557\" style=\"display:none;\"><div class=\"tp_bibtex_entry\"><pre>@inproceedings{ngouanfouo_detecting_2025,<br \/>\r\ntitle = {Detecting Machine-Generated Text using Grammatical Features},<br \/>\r\nauthor = {C. Ngouanfouo and A. Davoust},<br \/>\r\nurl = {https:\/\/www.scopus.com\/inward\/record.uri?eid=2-s2.0-105031903675&doi=10.1109%2FICTAI66417.2025.00123&partnerID=40&md5=5783b8797a3425f9dfa737343ee757d2},<br \/>\r\ndoi = {10.1109\/ICTAI66417.2025.00123},<br \/>\r\nisbn = {10823409 (ISSN); 979-833154919-0 (ISBN)},<br \/>\r\nyear  = {2025},<br \/>\r\ndate = {2025-01-01},<br \/>\r\nbooktitle = {Proc. Int. Conf. Tools Artif. Intell. ICTAI},<br \/>\r\npages = {843\u2013848},<br \/>\r\npublisher = {IEEE Computer Society},<br \/>\r\nabstract = {Large Language Models (LLMs) have advanced natural language generation but pose ethical and practical challenges, making it crucial to detect machine-generated texts. Traditional detection methods rely on complex, hard-to-interpret neural encodings and model-specific features like perplexity. This study explores whether grammatical patterns-specifically sequences of parts of speech (POS), including punctuation and symbols-can distinguish machine-written texts from human ones. Using a CNN classifier on POS sequences, the approach achieves nearly 90 % accuracy on a benchmark dataset. Combining POS-based features with neural embeddings improves performance, and the model shows robustness against adversarial attacks, though it is less effective on short texts. \u00a9 2025 IEEE.},<br \/>\r\nkeywords = {},<br \/>\r\npubstate = {published},<br \/>\r\ntppubtype = {inproceedings}<br \/>\r\n}<br \/>\r\n<\/pre><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('557','tp_bibtex')\">Close<\/a><\/p><\/div><div class=\"tp_abstract\" id=\"tp_abstract_557\" style=\"display:none;\"><div class=\"tp_abstract_entry\">Large Language Models (LLMs) have advanced natural language generation but pose ethical and practical challenges, making it crucial to detect machine-generated texts. Traditional detection methods rely on complex, hard-to-interpret neural encodings and model-specific features like perplexity. This study explores whether grammatical patterns-specifically sequences of parts of speech (POS), including punctuation and symbols-can distinguish machine-written texts from human ones. Using a CNN classifier on POS sequences, the approach achieves nearly 90 % accuracy on a benchmark dataset. Combining POS-based features with neural embeddings improves performance, and the model shows robustness against adversarial attacks, though it is less effective on short texts. \u00a9 2025 IEEE.<\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('557','tp_abstract')\">Close<\/a><\/p><\/div><\/td><\/tr><\/table><\/div><\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-5558b72 elementor-hidden-desktop elementor-hidden-tablet elementor-hidden-mobile e-flex e-con-boxed e-con e-parent\" data-id=\"5558b72\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-92ea5b7 elementor-widget elementor-widget-heading\" data-id=\"92ea5b7\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Some Heading<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-de7d7a3 elementor-hidden-desktop elementor-hidden-tablet elementor-hidden-mobile elementor-widget elementor-widget-text-editor\" data-id=\"de7d7a3\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>If needed, this section can contain some or all of the following:<\/p>\n<ul>\n<li>Recent news, updates, or upcoming events related to the professor or their department, such as guest lectures, seminars, and conferences.<\/li>\n<li>Social media feed.<\/li>\n<li>A quote from the professor about their philosophy on education and research or a testimonial from a peer or student adding a personal and inspirational element to the page, placing this information just above the share icons can give visitors current and relevant reasons to engage and share.<\/li>\n<li>Call to Action to attend or participate in some even.<\/li>\n<li>Contact Form<\/li>\n<li>Subscribe form<\/li>\n<\/ul>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-931098e e-flex e-con-boxed e-con e-parent\" data-id=\"931098e\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-ccca712 elementor-widget__width-auto eael-dual-header-content-align-center elementor-widget elementor-widget-eael-dual-color-header\" data-id=\"ccca712\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"eael-dual-color-header.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t<div class=\"eael-dual-header\">\n\t\t\t\t<h2 class=\"title eael-dch-title\"><span class=\"eael-dch-title-text eael-dch-title-lead lead solid-color\">Share Professor Alan Davoust<\/span> <span class=\"eael-dch-title-text\">publications with your network!<\/span><\/h2><div class=\"eael-dch-separator-wrap\"><span class=\"separator-one\"><\/span>\n\t\t\t<span class=\"separator-two\"><\/span><\/div>\t\t\t<\/div>\n\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"<p>Welcome If needed, this section can contain some or all of the following: A large, engaging image of the university, department, or an abstract representation of the academic field can set a professional and inspiring tone. 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