Abstract

The rapid advancement of Artificial Intelligence (AI) and Machine Learning (ML) has enabled significant progress in analysing complex and high-dimensional datasets. However, many modern AI models, particularly deep learning systems, function as black-box mechanisms where the internal decision-making process remains difficult to interpret. This limitation creates challenges in critical domains such as healthcare, finance, autonomous systems, and scientific research, where transparency and reliability are essential. Topological Data Analysis (TDA), based on concepts from computational topology and persistent homology, provides a mathematical approach for identifying structural patterns within complex data spaces. This research paper proposes a conceptual framework for integrating TDA with Artificial Intelligence to improve model robustness, feature representation, and explainability. The study reviews existing developments in persistent homology, topological feature extraction, and explainable AI while analysing their potential integration into machine learning pipelines.

Author

Krishan Kumar Srivastava & Rachit Kumar  ( Pages 91-100 )
Email:kksrivastava1234@gmail.com
Affiliation: Department of Mathematics, North East Cristian University, Dimapur, Nagaland, India      DOI: https://doi.org/10.58517/ITMSC.2025.18106

Keyword

Topological Data Analysis, Artificial Intelligence, Machine Learning, Persistent Homology, Explainable AI, Deep Learning, Persistence Diagram, Robust AI, Computational Topology.

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