CriX: Intersection of Crime, Demographics and Explainable AI

Muhammad Ashar Reza, Aaditya Bisaria, S. Advaitha, Alekhya Ponnekanti, Arti Arya

2025

Abstract

Crime prediction and analysis often rely on crime statistics but neglect the potential influences of demographic factors. Each locality possesses unique characteristics indicating that a ’one-size-fits-all’ methodology is in-adequate. This research presents a framework CriX that incorporates demographic factors to help understand and address localised crime. At the root level, identifying and predicting crime hotspots is essential for providing context in training the language model; therefore, ST-DBSCAN and LSTM models are respectively used on a custom-made dataset. InLegalBERT (Paul et al., 2023), which is pre-trained on Indian legal data, helps generate embeddings for the large corpus of crime hotspot, demographic and legal data. These embeddings are stored in a FAISS vector store, allowing for dynamic retrieval using RAG techniques. The generated embeddings are then fed into MistralAI offering a textual solution. These outputs are further refined using zero shot learning increasing model performance. The proposed framework achieved a validation accuracy of over 82% for crime hotspot predictions. The LLM also showcased substantial scores for Compactness, Fidelity and Completeness, giving an average score of 4.18 out of 5, outperforming baseline models. This approach enhances the interpretability of legal models by incorporating the concepts of Explainable AI (XAI).

Paper Citation

in Harvard Style

Reza M., Bisaria A., Advaitha S., Ponnekanti A. and Arya A. (2025). CriX: Intersection of Crime, Demographics and Explainable AI. In Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART; ISBN 978-989-758-737-5, SciTePress, pages 714-725. DOI: 10.5220/0013316200003890

in Bibtex Style

@conference{icaart25,
author={Muhammad Reza and Aaditya Bisaria and S. Advaitha and Alekhya Ponnekanti and Arti Arya},
title={CriX: Intersection of Crime, Demographics and Explainable AI},
booktitle={Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART},
year={2025},
pages={714-725},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013316200003890},
isbn={978-989-758-737-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART
TI - CriX: Intersection of Crime, Demographics and Explainable AI
SN - 978-989-758-737-5
AU - Reza M.
AU - Bisaria A.
AU - Advaitha S.
AU - Ponnekanti A.
AU - Arya A.
PY - 2025
SP - 714
EP - 725
DO - 10.5220/0013316200003890
PB - SciTePress