The Disagreement Dilemma in Explainable AI: Can Bias Reduction Bridge the Gap

The Disagreement Dilemma in Explainable AI: Can Bias Reduction Bridge the Gap | 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 The Disagreement Dilemma in Explainable AI: Can Bias Reduction Bridge the Gap Nitanshi Bhardwaj, Gaurav Parashar This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4193128/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 31 Jan, 2025 Read the published version in International Journal of System Assurance Engineering and Management → Version 1 posted 5 You are reading this latest preprint version Abstract Explainable AI (XAI) is an emerging field of research since the spread of AI in multifarious fields. The opacity and inherent black-box nature of the advanced machine learning models create a lack of transparency in them leading to the insufficiency in societal recognition. The increasing dependence on AI across diverse sectors has created the need for informed decision-making of the numerous predictive models used. XAI strives to close this divide by providing an explanation of the decision-making process, promoting trust, ensuring adherence to regulations, and cultivating societal approval. Various post-hoc techniques including well-known methods like LIME, SHAP, Integrated Gradients, Partial Dependence Plot, and Accumulated Local Effects have been proposed to decipher the intricacies of complex AI models. In the context of post hoc explanatory methods for machine learning models there arises a conflict known as the Disagreement problem where different explanation techniques provide differing interpretations of the same model. In this study, we aim to find whether reducing the bias in the dataset could lead to XAI explanations that do not disagree. The study thoroughly analyzes this problem, examining various widely recognized explanation methods. Explainable AI bias lime shap Full Text Cite Share Download PDF Status: Published Journal Publication published 31 Jan, 2025 Read the published version in International Journal of System Assurance Engineering and Management → Version 1 posted Editorial decision: Major revisions 24 Aug, 2024 Reviewers agreed at journal 03 Jul, 2024 Reviewers invited by journal 26 Jun, 2024 Editor invited by journal 02 Apr, 2024 First submitted to journal 01 Apr, 2024 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. 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