Unravelling the Black Box: Explainable AI Approaches for Robust Hate Speech Detection

Unravelling the Black Box: Explainable AI Approaches for Robust Hate Speech Detection

Registration ID: IJNRD_300381

Published ID: IJNRD2402375

DOI: Click Here to Get

Dipti Mittal , Harmeet Singh , Sita Rani

explainable artificial intelligence; hate speech detection; offensive languages; LIME; BERT; neural networks

The potential of flexible and multifaceted characteristics in hate speech detection by deep learning models is found within Explainable Artificial Intelligence (XAI). The goal of this research was to comprehend the decision-making process through interpreting and explaining complex AI model decisions. Two datasets were chosen for demonstrating XAI's implementation into detecting cases of hate speech, which underwent data preprocessing with steps such as text cleaning, tokenization, lemmatization etc., followed by categorically simplifying them for training purposes. Exploratory analysis conducted on said dataset revealed patterns and insights that aided several pre- existing models from Google Jigsaw' including Decision Trees, K-Nearest Neighbors Multinomial Naïve Bayes Random Forest Logistic Regression Long Short-Term Memory among others where LSTM achieved an incredible accuracy rate at 97.6%. For explainability techniques like LIME or Local Interpretable Model Agnostic Explanations can be utilized upon using the HateXplain dataset while Variants were built atop BERT(Bidirectional Encoder Representations From Transformers) called BERT+ANN(Artificial Neural Network) with a result yieldting 93.l55% Accuracy Alongside By Using Benchmark ERASER(Evaluating Rationales And Simple English Reasoning), another variant labeled BETT + MLP(Multilayer Perceptron) yielded impressive results up to 93 .67 % accuracy performance metrics standardized provide good performance.

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"Unravelling the Black Box: Explainable AI Approaches for Robust Hate Speech Detection", IJNRD - INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT (www.IJNRD.org), ISSN:2456-4184, Vol.9, Issue 9, page no.d721-d735, September-2024, Available :https://ijnrd.org/papers/IJNRD2402375.pdf

Volume 9 Issue 9, September-2024

Pages : d721-d735

Paper Reg. ID: IJNRD_300381

Published Paper Id: IJNRD2402375

Downloads: 000121138

Research Area: Science and Technology

Country: -, -, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2402375.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2402375

Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

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This work is licensed under a Creative Commons Attribution 4.0 International License and The Open Definition

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