Cracking the Code: Self-Explaining AI Models for Transparent Decision Making in Complex Algorithms.

Author(s)	Aatmaj Amol Salunke
Country	India
Abstract	This research paper explores self-explaining AI models that bridge the gap between complex black-box algorithms and human interpretability. The study focuses on techniques like LIME, SHAP, attention mechanisms, and rule-based systems to create locally interpretable models. By providing transparent and understandable explanations for AI predictions, these models enhance user trust and comprehension. Real-world applications in healthcare, finance, and autonomous systems are evaluated to demonstrate the effectiveness of self-explaining AI models. Ethical considerations regarding fairness, bias, and accountability in AI decision-making are also addressed. The findings underscore the potential of such models to unlock the mysteries of complex algorithms, making AI more accessible and interpretable for diverse applications.
Keywords	Self-Explaining AI, Interpretability, AI Black-Boxes, Trust in AI, Explainable AI, Ethical AI
Field	Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In	Volume 5, Issue 4, July-August 2023
Published On	2023-08-16
DOI	https://doi.org/10.36948/ijfmr.2023.v05i04.5395

About IJFMR Fees & Payment Current Issue Publication Archive	Submit Research Paper Track Submission Status Publication Guidelines Publication Ethics Peer Review & Plagiarism	Join as a Reviewer Editors & Reviewers Reviewer Referral Program Get Reviewer Membership Certi.	Website/Journal Policies Usage Policy Content Policies Privacy Policy

Contact Us		+91-9687-828-838	editor@ijfmr.com

International Journal For Multidisciplinary Research