Metrology-Inspired Methods to Assess the Biases of Artificial Intelligence Systems

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Assess the Biases of Artificial Intelligence Systems</title> <meta name="description" content="Metrology-Inspired Methods to Assess the Biases of Artificial Intelligence Systems"> <meta name="keywords" content="Artificial intelligence, metrology, measurement uncertainty, prediction error, bias, machine learning algorithms, probabilistic models, inter-laboratory comparison, data analysis, data reliability, bias impact assessment, bias measurement."> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <meta name="citation_title" content="Metrology-Inspired Methods to Assess the Biases of Artificial Intelligence Systems"> <meta name="citation_author" content="Belkacem Laimouche"> <meta name="citation_publication_date" content="2024/05/07"> <meta name="citation_journal_title" content="International Journal of Mechanical and Industrial Engineering"> <meta name="citation_volume" content="18"> <meta 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href="https://publications.waset.org/search?q=Belkacem%20Laimouche">Belkacem Laimouche</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>With the field of Artificial Intelligence (AI) experiencing exponential growth, fueled by technological advancements that pave the way for increasingly innovative and promising applications, there is an escalating need to develop rigorous methods for assessing their performance in pursuit of transparency and equity. This article proposes a metrology-inspired statistical framework for evaluating bias and explainability in AI systems. Drawing from the principles of metrology, we propose a pioneering approach, using a concrete example, to evaluate the accuracy and precision of AI models, as well as to quantify the sources of measurement uncertainty that can lead to bias in their predictions. Furthermore, we explore a statistical approach for evaluating the explainability of AI systems based on their ability to provide interpretable and transparent explanations of their predictions.</p> <iframe src="https://publications.waset.org/10013629.pdf" style="width:100%; height:400px;" frameborder="0"></iframe> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Artificial%20intelligence" title="Artificial intelligence">Artificial intelligence</a>, <a href="https://publications.waset.org/search?q=metrology" title=" metrology"> metrology</a>, <a href="https://publications.waset.org/search?q=measurement%20uncertainty" title=" measurement uncertainty"> measurement uncertainty</a>, <a href="https://publications.waset.org/search?q=prediction%20error" title=" prediction error"> prediction error</a>, <a href="https://publications.waset.org/search?q=bias" title=" bias"> bias</a>, <a href="https://publications.waset.org/search?q=machine%20learning%20algorithms" title=" machine learning algorithms"> machine learning algorithms</a>, <a href="https://publications.waset.org/search?q=probabilistic%20models" title=" probabilistic models"> probabilistic models</a>, <a href="https://publications.waset.org/search?q=inter-laboratory%20comparison" title=" inter-laboratory comparison"> inter-laboratory comparison</a>, <a href="https://publications.waset.org/search?q=data%20analysis" title=" data analysis"> data analysis</a>, <a href="https://publications.waset.org/search?q=data%20reliability" title=" data reliability"> data reliability</a>, <a href="https://publications.waset.org/search?q=bias%20impact%20assessment" title=" bias impact assessment"> bias impact assessment</a>, <a href="https://publications.waset.org/search?q=bias%20measurement." title=" bias measurement."> bias measurement.</a> </p> <a href="https://publications.waset.org/10013629/metrology-inspired-methods-to-assess-the-biases-of-artificial-intelligence-systems" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/10013629/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/10013629/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/10013629/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/10013629/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/10013629/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/10013629/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/10013629/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/10013629/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/10013629/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/10013629/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/10013629.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">143</span> </span> <p class="card-text"><strong>References:</strong></p> <br>[1] JCGM GUM-6:2020 Guide to the expression of uncertainty in measurement Part 6: Developing and using measurement models <br>[2] NF ISO 5725-2 (1994) Accuracy (trueness and precision) of measurement results and methods. Part 2 Basic method for the determination of the repeatability and reproducibility of a standard measurement method <br>[3] ISO/TS 21748: Guide to uncertainty estimation using interlaboratory study data <br>[4] Guide ISO 43 Proficiency testing of laboratories by intercomparison <br>[5] NF ISO 13528 December 2005, Statistical methods used in proficiency testing by interlaboratory comparisons <br>[6] NF EN ISO/CEI 17043 (April 2010), Conformity assessment - General requirements for proficiency testing <br>[7] FD ISO GUIDE 34 May 2010 General requirements for the competence of reference material producers. </div> </div> </div> </main> <footer> <div id="infolinks" class="pt-3 pb-2"> <div class="container"> <div style="background-color:#f5f5f5;" class="p-3"> <div class="row"> <div class="col-md-2"> <ul class="list-unstyled"> About <li><a href="https://waset.org/page/support">About Us</a></li> <li><a href="https://waset.org/page/support#legal-information">Legal</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/WASET-16th-foundational-anniversary.pdf">WASET celebrates its 16th foundational anniversary</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Account <li><a href="https://waset.org/profile">My Account</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Explore <li><a href="https://waset.org/disciplines">Disciplines</a></li> <li><a href="https://waset.org/conferences">Conferences</a></li> <li><a href="https://waset.org/conference-programs">Conference Program</a></li> <li><a href="https://waset.org/committees">Committees</a></li> <li><a href="https://publications.waset.org">Publications</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Research <li><a href="https://publications.waset.org/abstracts">Abstracts</a></li> <li><a href="https://publications.waset.org">Periodicals</a></li> <li><a href="https://publications.waset.org/archive">Archive</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Open Science <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Open-Science-Philosophy.pdf">Open Science Philosophy</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Open-Science-Award.pdf">Open Science Award</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Open-Society-Open-Science-and-Open-Innovation.pdf">Open Innovation</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Postdoctoral-Fellowship-Award.pdf">Postdoctoral Fellowship Award</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Scholarly-Research-Review.pdf">Scholarly Research Review</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Support <li><a href="https://waset.org/page/support">Support</a></li> <li><a href="https://waset.org/profile/messages/create">Contact Us</a></li> <li><a href="https://waset.org/profile/messages/create">Report Abuse</a></li> </ul> </div> </div> </div> </div> </div> <div class="container text-center"> <hr style="margin-top:0;margin-bottom:.3rem;"> <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank" class="text-muted small">Creative Commons Attribution 4.0 International License</a> <div id="copy" class="mt-2">© 2024 World Academy of Science, Engineering and Technology</div> </div> </footer> <a href="javascript:" id="return-to-top"><i class="fas fa-arrow-up"></i></a> <div class="modal" id="modal-template"> <div class="modal-dialog"> <div class="modal-content"> <div class="row m-0 mt-1"> <div class="col-md-12"> <button type="button" class="close" data-dismiss="modal" aria-label="Close"><span aria-hidden="true">×</span></button> </div> </div> <div class="modal-body"></div> </div> </div> </div> <script src="https://cdn.waset.org/static/plugins/jquery-3.3.1.min.js"></script> <script src="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/js/bootstrap.bundle.min.js"></script> <script src="https://cdn.waset.org/static/js/site.js?v=150220211556"></script> <script> jQuery(document).ready(function() { /*jQuery.get("https://publications.waset.org/xhr/user-menu", function (response) { jQuery('#mainNavMenu').append(response); });*/ jQuery.get({ url: "https://publications.waset.org/xhr/user-menu", cache: false }).then(function(response){ jQuery('#mainNavMenu').append(response); }); }); </script> </body> </html>

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Metrology-Inspired Methods to Assess the Biases of Artificial Intelligence Systems