ItemConference Paper

Explaining Autism Diagnosis Model Through Local Interpretability Techniques - A Post-hoc Approach

Title
Explaining Autism Diagnosis Model Through Local Interpretability Techniques - A Post-hoc Approach
Creator
Sujana D.S.; Augustine D.P.
Description
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.
Source
2023 International Conference on Data Science, Agents and Artificial Intelligence, ICDSAAI 2023
Date
2023-01-01
Publisher
Institute of Electrical and Electronics Engineers Inc.
Subject
anchor; explainability; lime; ml interpretability; shap; xai
Coverage
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
Rights
Restricted Access
Relation
ISBN: 979-835034891-0
Format
Online
Language
English
Type
Conference paper

Collection

Conference Papers

Citation

Sujana D.S.; Augustine D.P., “Explaining Autism Diagnosis Model Through Local Interpretability Techniques - A Post-hoc Approach,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 25, 2025, https://archives.christuniversity.in/items/show/19652.

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