Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Conference Papers Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Explaining Autism Diagnosis Model Through Local Interpretability Techniques - A Post-hoc Approach Subject The topic of the resource anchor; explainability; lime; ml interpretability; shap; xai Description An account of the resource In this era of machine learning and deep learning algorithms dominating the Artificial Intelligence (AI) world, the trustworthiness of these black box models is still questionable. Life-caring sectors like healthcare and banking make use of these black box models as assistance in critical decision-making processes, but the degree of reliability of these decisions is still uncertain. This is because these black box models will not reveal the causation of the predicted outcome. However, creating an interpretable model that can explain the internal workings of these black box models can provide some reliable insights and trustable justifications for the predicted outcome. This study aimed to create an interpretable model for autism diagnosis which can give some trustable explanations for its predicted outcome. Using local interpretability methods such as LIME, SHAP, and Anchors the predicted outcome for each instance is explained well with some standard visual representations. As a result, this study developed an interpretable autism diagnosis model with an accuracy rate of 91.37% and with good local model explanations. 2023 IEEE. Creator An entity primarily responsible for making the resource Sujana D.S.; Augustine D.P. Source A related resource from which the described resource is derived 2023 International Conference on Data Science, Agents and Artificial Intelligence, ICDSAAI 2023 Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Date A point or period of time associated with an event in the lifecycle of the resource 2023-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICDSAAI59313.2023.10452575" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICDSAAI59313.2023.10452575</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85187798451&amp;doi=10.1109%2FICDSAAI59313.2023.10452575&amp;partnerID=40&amp;md5=f2a075f0e0a1452551c7349b322ef9a4" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85187798451&amp;doi=10.1109%2fICDSAAI59313.2023.10452575&amp;partnerID=40&amp;md5=f2a075f0e0a1452551c7349b322ef9a4</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 979-835034891-0 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Conference paper Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Sujana D.S., Christ (Deemed to Be University), Department of Computer Science, Bangalore, India; Augustine D.P., Christ (Deemed to Be University), Department of Computer Science, Bangalore, India