Shajalal, Md
Preferred name
Shajalal, Md
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Publication Access status: Open Access , Arabic Sentiment Analysis with Noisy Deep Explainable ModelSentiment Analysis (SA) is an essential task for numerous real-world applications. However, the majority of SA research focuses on high-resource languages such as English and Chinese, while limited-resource languages like Arabic and Bengali receive less attention. Additionally, existing Arabic sentiment analysis methods based on advanced artificial intelligence (AI) approaches tend to operate as black boxes, making it challenging to comprehend the reasoning behind their predictions. This paper proposes an explainable sentiment classification framework for the Arabic language. We introduce a noise layer to different deep learning (DL) models, including BiLSTM and CNN-BiLSTM, to address the issue of overfitting. The proposed framework enables the explanation of specific predictions by training a local surrogate explainable model, shedding light on the reasons behind each sentiment prediction (positive or negative). Experiments were conducted on publicly available benchmark Arabic SA datasets, and the results demonstrated that the inclusion of noise layers in the DL model improves performance for the Arabic language by mitigating overfitting. Our method also outperformed several state-of-the-art approaches. Moreover, the introduction of explainability with the noise layer enhances transparency and accountability, making the model suitable for practical adoption in AI-enabled systems.Source Type:InProceedings2 5 - Some of the metrics are blocked by yourconsent settings
Publication Access status: Open Access , Explaining AI Decisions: Towards Achieving Human-Centered Explainability in Smart Home EnvironmentsSmart home systems are gaining popularity as homeowners strive to enhance their living and working environments while minimizing energy consumption. However, the adoption of artificial intelligence (AI)-enabled decision-making models in smart home systems faces challenges due to the complexity and black-box nature of these systems, leading to concerns about explainability, trust, transparency, accountability, and fairness. The emerging field of explainable artificial intelligence (XAI) addresses these issues by providing explanations for the models’ decisions and actions. While state-of-the-art XAI methods are beneficial for AI developers and practitioners, they may not be easily understood by general users, particularly household members. This paper advocates for human-centered XAI methods, emphasizing the importance of delivering readily comprehensible explanations to enhance user satisfaction and drive the adoption of smart home systems. We review state-of-the-art XAI methods and prior studies focusing on human-centered explanations for general users in the context of smart home applications. Through experiments on two smart home application scenarios, we demonstrate that explanations generated by prominent XAI techniques might not be effective in helping users understand and make decisions. We thus argue for the necessity of a human-centric approach in representing explanations in smart home systems and highlight relevant human-computer interaction (HCI) methodologies, including user studies, prototyping, technology probes analysis, and heuristic evaluation, that can be employed to generate and present human-centered explanations to users.Source Type:InProceedings1 4 - Some of the metrics are blocked by yourconsent settings
Publication Access status: Open Access , Towards Human-Centered Actionable Explainable AI-enabled Systems(2025)Recently, the applications of complex artificial Intelligence (AI) models have increased exponentially in almost every sector due to the enormous advancement of computing power and the availability of high-quality annotated data for training complex machine learning (ML) models. Generally, AI models are very complex in structure, and they often need to learn thousands, even millions, of parameters in the training phase. Though the predictions are accurate, due to the complex decision-making process, the predictions from such AI models are not understandable to users. Hence, AI systems lack explainability, transparency, and trustworthiness in making the decision explainable to the users. AI models with such complexity are often referred to as black-box models. To interpret the black-box AI models, recently, there has been a high interest in the AI research community concentrating on extracting facts and rationale to explain the reasons behind the prediction and overall models' decision-making priorities. The field that practices interpreting complex AI models and explaining the predictions to uncover the reasons behind particular predictions is known as eXplainable Artificial Intelligence (XAI). Improvements in interpreting ML models have been evident in this decade. However, the current explainability techniques are helpful for AI practitioners in the way that they can employ explanations to debug and eventually improve the models' performance. However, the primary objective of XAI is to help laypeople understand the predictions by providing human-centric explanations, which will eventually increase transparency and trust and lead to faster AI adoption in real-world applications. A significant gap exists in achieving human-centered explainability for AI systems due to associated challenges, including user experience variability, context sensitivity, bias and data deficiency, and actionability. This dissertation aims to advocate the human-understandable explainability of AI-enabled systems and introduces explainable models in different real-world application scenarios. We strive to answer research questions, including i) How can we achieve high-performance ML models addressing technical challenges, including data imbalance, data inadequacy, and model bias? ii) How do explanations vary across different application contexts? iii) What underlying facts and rationale should be considered when explaining prediction for a given context? and iv) How could we achieve actionable explanations? We adopted an exploratory, experimental approach to answering these questions by conducting a wide range of experiments, introducing explainability techniques, and demonstrating explanations in three application areas: smart home, business, and natural language processing (NLP). After carefully selecting application scenarios considering the mentioned questions, this thesis proposed multiple high-performance ML models for energy demand forecasting, occupants' thermal comfort preference modeling, product backorder prediction, multi-class patent classification, and fake review identification. Then, it introduced explainability to provide comprehensible, actionable explanations so that users and stakeholders could understand the predictions and take necessary action accordingly. The results from a wide range of experiments demonstrated high performance compared to state-of-the-art methods. They provided explanations that capture relevant facts and rationale to make users understand the proposed ML models' predictions and overall priorities. The technical and empirical evaluation of the generated explanations for explainable AI-enabled systems highlighted what information needs to be considered and how they should be represented in explanations. The broad contribution of this thesis is three-fold: i) We achieved high-performance ML models in different application areas addressing the challenges, including data inadequacy and extreme imbalance; ii) With a wide range of experiments, this thesis gives a holistic conclusion on what facts and rationale should be employed in generating explanations for a given application context; iii) Lastly, this dissertation highlighted how we can achieve actionable explanations so that users can take necessary actions to earn more system efficiency in the given application context.Source Type:Doctoral Thesis11 10
