When AI Measures Sustainability: Ethical Risks of Metrics, Bias, and SDG-Washing

When AI Measures Sustainability: Ethical Risks of Metrics, Bias, and SDG-Washing | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article When AI Measures Sustainability: Ethical Risks of Metrics, Bias, and SDG-Washing Abiodun F Ibidunmoye, Grace A. Eneano, Ogechi M. Ikeakaonwu, Ngozi B. Umoru, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8564828/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Artificial intelligence (AI) and business analytics are increasingly promoted as instrumental to achieving the United Nations Sustainable Development Goals (SDGs). Governments, corporations, and international organisations now deploy AI-driven indicators, dashboards, and optimisation systems to monitor sustainability performance, guide policy, and signal social responsibility. Despite this growing reliance, the ethical implications of AI-enabled sustainability analytics remain insufficiently examined. This paper critically investigates whether AI-driven business analytics genuinely advance the SDGs or risk devolving into ethical tokenism through SDG-washing, metric manipulation, and normative bias. Drawing on interdisciplinary literature in AI ethics, sustainability governance, and development studies, the paper argues that many AI-enabled SDG initiatives prioritise measurable indicators over substantive social and environmental outcomes. Key ethical challenges are examined, including misalignment between analytics key performance indicators and SDG intent, difficulties in quantifying social values, and biases embedded within sustainability datasets. The paper proposes an ethical governance framework for SDG-oriented AI analytics that emphasises value alignment, contextual sensitivity, outcome-focused evaluation, and accountability. It concludes that AI can contribute meaningfully to sustainable development only when ethical design and governance mechanisms prevent metric-driven compliance from substituting for genuine progress. AI ethics Sustainable Development Goals business analytics SDG-washing normative bias sustainability governance Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8564828","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":575842218,"identity":"0bfc7880-db73-4f4c-83c2-74c6af2a6dfd","order_by":0,"name":"Abiodun F Ibidunmoye","email":"","orcid":"","institution":"University of Lagos","correspondingAuthor":false,"prefix":"","firstName":"Abiodun","middleName":"F","lastName":"Ibidunmoye","suffix":""},{"id":575842220,"identity":"3116218e-8857-4c02-9b1b-5a69214f139c","order_by":1,"name":"Grace A. 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