SAF: Stakeholders’ Agreement on Fairness in the Practice of Machine Learning Development.
This 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 pre...
| Publicado en: | Science & Engineering Ethics Vol. 29; no. 4; pp. 1 - 20 |
|---|---|
| Autores principales: | , |
| Formato: | Artículo |
| Publicado: |
Springer Nature
Aug2023
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=166329586&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 166329586 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 13533452 GNI jtl: Science & Engineering Ethics issn: 13533452 maglogo: N pubinfo: dt: Aug2023 vid: 29 iid: 4 pid: 237 pub: Springer Nature artinfo: ui: 166329586 10.1007/s11948-023-00448-y ppf: 1 ppct: 19 formats: fmt: – @attributes: type: T – @attributes: type: P size: 2.5MB tig: atl: SAF: Stakeholders’ Agreement on Fairness in the Practice of Machine Learning Development. aug: au: Curto, Georgina Comim, Flavio affil: University of Notre Dame, Notre Dame, USA IQS School of Management, Universitat Ramon Llull, Barcelona, Spain sug: keyword: Artificial Intelligence Bias Discrimination Fairness Pro-Ethical Design Trustworthy AI ab: This 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. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y custom: Science & Engineering Ethics is a copyright of Springer, 2023. All Rights Reserved. item: Science & Engineering Ethics holder: Springer Nature dt: @attributes: year: 2023 holdings: @attributes: islocal: N |
|---|