[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125275-en":3,"doc-seo-125275-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},125275,2336464648746,"Skyler","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","SAF: Stakeholders’ Agreement on Fairness in the Practice of Machine Learning Development","The paper explains why bias in machine learning cannot be fully eliminated and introduces an end-to-end methodology that operationalizes justice and fairness as an ongoing agreement among stakeholders in ML development. It presents a pro-ethical iterative process designed to challenge asymmetric power relations that shape fairness decisions in the design of ML systems. The method supports teams in identifying, mitigating, and continuously monitoring bias at every development step, and provides practical guidance for communicating imperfect bias-related trade-offs to users.","Science and Engineering Ethics (2023) 29:29  \n[https://doi.org/10.1007/s1](https://doi.org/10.1007/s1)1948-023-00448-y  \nORIGINAL RESEARCH/SCHOLARSHIP  \nSAF: Stakeholders’ Agreement on Fairness in the Practice of Machine Learning Development  \nGeorgina Curto1 · Flavio Comim2  \nReceived: 26 December 2021 / Accepted: 16 June 2023 / Published online: 24 July 2023 © The Author(s) 2023  \nAbstract  \nThis paper clarifies why bias cannot be completely mitigated in Machine Learning (ML) and proposes an end-to-end methodology to translate the ethical principle of justice and fairness into the practice of ML development as an ongoing agreement with stakeholders. The pro-ethical iterative process presented in the paper aims to challenge asymmetric power dynamics in the fairness decision making within ML design and support ML development teams to identify, mitigate and monitor bias at each step of ML systems development. The process also provides guidance on how to explain the always imperfect trade-offs in terms of bias to users.  \nKeywords Bias · Artificial Intelligence · Trustworthy AI · Fairness · Discrimination · Pro-Ethical Design  \nIntroduction  \nDiscrimination and bias in AI are still unresolved topics in the current information civilization (Zuboff, 2019) . While AI has the potential to facilitate the achievement of all United Nations Sustainable Goals, it can also widen existing social gaps by reproducing and often aggravating societal bias (Vinuesa et al., 2020) . ML systems in particular have been found to often exacerbate representational and allocational harm to vulnerable salient groups (Suresh & Guttag, 2021). As a result, these groups not only receive demeaning treatment, but also less resources and opportunities. AIamplified bias has been identified in critical services such as education, health and justice (Floridi, 2020) .  \n􀀍 Georgina Curto[gcurtore@nd.edu](gcurtore@nd.edu)  \n1 University of Notre Dame, Notre Dame, USA  \n2 IQS School of Management, Universitat Ramon Llull, Barcelona, Spain  \nML bias has other particularities that deserve special attention. Users are often not aware of it and developers cannot always explain it (Barocas & Selbst, 2016) . In this context, governmental efforts to regulate AI have gained traction in the past few years (White House, 2016; European Commission, 2021) . In addition, there has been a proliferation of ethical guidelines (Algorithm Watch, 2021) in what has been described as a “moral panic”(Ess, 2020) . These have been found to converge on specific topics (Hagendorff, 2020; Zeng et al., 2019) and have been summarized as 5 ethical principles: transparency, justice and fairness, non-maleficence, responsibility and privacy (Jobin, 2019).The scope of this article focuses on the principle of justice and fairness and facilitates the principle of transparency by guiding which specific steps of the fairness decision making process should be openly disclosed.  \nIn the nascent field of AI ethics, these ethical principles have been qualified as appropriate but too abstract. AI development teams find them difficult to apply in practice. Existing AI ethics guidelines focus the effort on the “what” and fall short on clarifying the operationalization of AI ethics (Floridi, 2019; Morley et al., 2021a; Morley et al., 2021; Vakkuri et al., 2020; Vakkuri and Kemell, 2019) . As a result, counterproductive practices such as ethics shopping, ethics bluewashing, ethics lobbying, ethics dumping or ethics shirking are prone to flourish (Floridi, 2019) . An urgency has been identified to translate theoretical principles into practical inclusive processes (Harrison et al., 2020) . This article has the objective to answer the question: how can the principle of justice and fairness be applied into the practice of ML development? With that aim, the paper first provides a conceptual framework, rooted in social and cognitive sciences, describing different interpretations of fairness. Then, we present the Stakeho","cbCaib45nkNzTsJR","https://ap.wps.com/l/cbCaib45nkNzTsJR","pdf",546358,1,19,"English","en",105,"# Introduction\n## Bias, discrimination, and societal impact\n## Applying AI ethics principles in practice\n# Methodology: Stakeholders’ Agreement on Fairness (SAF)\n## Iterative pro-ethical process for fairness decisions\n## Transparency of fairness trade-offs","[{\"question\":\"Why can bias not be completely mitigated in machine learning development?\",\"answer\":\"Bias cannot be fully eliminated, so fairness work must address ongoing and imperfect trade-offs rather than assuming complete removal. The paper frames fairness as a continuous practice within ML development.\"},{\"question\":\"What does the SAF methodology aim to achieve?\",\"answer\":\"SAF (Stakeholders’ Agreement on Fairness) is an iterative process that helps ML development teams support fairness decision making at each stage of design, testing, and deployment with active stakeholder participation.\"},{\"question\":\"How does the paper recommend communicating fairness-related trade-offs to users?\",\"answer\":\"The pro-ethical iterative process includes guidance on explaining the always imperfect bias-related trade-offs in a transparent way to users, while supporting teams to disclose relevant fairness decisions.\"}]","SAF: Stakeholders’ Agreement on Fairness in the Practice of Machine Learning Development | PDF",1785897847,48,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"saf-stakeholders-agreement-on-fairness-in-the-practice-of-machine-learning-development","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/saf-stakeholders-agreement-on-fairness-in-the-practice-of-machine-learning-development/125275/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why can bias not be completely mitigated in machine learning development?","Question",{"text":75,"@type":76},"Bias cannot be fully eliminated, so fairness work must address ongoing and imperfect trade-offs rather than assuming complete removal. The paper frames fairness as a continuous practice within ML development.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What does the SAF methodology aim to achieve?",{"text":80,"@type":76},"SAF (Stakeholders’ Agreement on Fairness) is an iterative process that helps ML development teams support fairness decision making at each stage of design, testing, and deployment with active stakeholder participation.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the paper recommend communicating fairness-related trade-offs to users?",{"text":84,"@type":76},"The pro-ethical iterative process includes guidance on explaining the always imperfect bias-related trade-offs in a transparent way to users, while supporting teams to disclose relevant fairness decisions.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},"General","general"]