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      Monitoring bias and fairness in machine learning models: A review

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            Abstract

            Introduction

            Machine learning algorithms are quickly gaining traction in both the private and public sectors for their ability to automate both simple and complex decision-making processes. The vast majority of economic sectors, including transportation, retail, advertisement, and energy, are being disrupted by widespread data digitization and the emerging technologies that leverage it. Computerized systems are being introduced in government operations to improve accuracy and objectivity, and AI is having an impact on democracy and governance [1]. Numerous businesses are using machine learning to analyze massive quantities of data, from calculating credit for loan applications to scanning legal contracts for errors to analyzing employee interactions with customers to detect inappropriate behavior. New tools make it easier than ever for developers to design and deploy machine-learning algorithms [2] [3].

            Content

            Author and article information

            Journal
            ScienceOpen Preprints
            ScienceOpen
            6 May 2021
            Affiliations
            [1 ] UNICAMP - Universidade Estadual de Campinas
            Article
            10.14293/S2199-1006.1.SOR-.PP59WRH.v1
            3cfeaa17-f010-4c10-8b89-3874eec83444

            This work has been published open access under Creative Commons Attribution License CC BY 4.0 , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Conditions, terms of use and publishing policy can be found at www.scienceopen.com .


            Data sharing not applicable to this article as no datasets were generated or analysed during the current study.
            Artificial intelligence

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