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Introduction:

Artificial Intelligence (AI) has made significant advancements in recent years, particularly in areas such as image recognition, natural language processing, and autonomous vehicles. However, as AI systems become more complex and sophisticated, the need for transparency and interpretability in their decision-making processes has become increasingly important. This has led to the emergence of Explainable AI (XAI), which refers to the ability of AI models to explain their reasoning and decision-making processes in a way that is understandable to humans.

Background of study:

The lack of transparency in AI systems has raised concerns about their reliability, accountability, and potential biases. In response to these challenges, researchers have begun developing interpretable models that can provide explanations for their predictions and decisions. These interpretable models aim to improve the trustworthiness and usability of AI systems across various domains, including healthcare, finance, and criminal justice.

Problem Statement:

Despite the growing interest in XAI, there is still a lack of consensus on what constitutes an interpretable model and how to evaluate its explanations. Additionally, the trade-off between accuracy and interpretability remains a key challenge in the development of XAI systems. This study seeks to address these issues by investigating different approaches to building interpretable AI models and evaluating their effectiveness in providing explanations for their decisions.

Objective of study:

The main objective of this thesis is to explore the concept of XAI and its implications for building interpretable AI models. Specifically, the study aims to:

1. Review the existing literature on XAI and interpretable models
2. Identify the key challenges and opportunities in the field of XAI
3. Design and implement an interpretable AI system
4. Evaluate the effectiveness of the interpretable model in providing explanations for its decisions

Limitation of study:

This study is limited to exploring the concept of XAI for interpretable models in a specific domain, such as healthcare or finance. The findings may not be generalizable to other domains or types of AI systems.

Scope of study:

The scope of this study includes reviewing the literature on XAI, designing and implementing an interpretable AI system, and evaluating the effectiveness of the model in providing explanations for its decisions. The study will focus on a specific domain to demonstrate the application of XAI in real-world scenarios.

Significance of study:

This study is significant because it addresses the growing need for transparency and interpretability in AI systems. By building interpretable models that can explain their decisions, this research aims to improve the trustworthiness and accountability of AI systems in various domains.

Structure of the Thesis:

This thesis is organized into five chapters. Chapter one includes the introduction, background of study, problem statement, objective of study, limitation of study, scope of study, significance of study, and structure of the thesis. Chapter two presents a literature review on XAI and interpretable models. Chapter three discusses the system design and methodology, while chapter four covers the system implementation. Finally, chapter five provides the conclusion and summary of the project thesis.

Definition of terms:

– Artificial Intelligence (AI): The simulation of human intelligence processes by machines, typically involving tasks such as learning, reasoning, and problem-solving.
– Explainable AI (XAI): The ability of AI models to provide explanations for their decisions and actions in a way that is understandable to humans.
– Interpretable Model: A model that can provide explanations for its predictions and decisions in a transparent and comprehensible manner.

Thesis Overview on Explainable AI for Interpretable Models:

Artificial Intelligence (AI) has become increasingly prevalent in various industries, including healthcare, finance, and autonomous vehicles. However, as AI systems become more complex and sophisticated, the need for transparency and interpretability in their decision-making processes has become a growing concern. This has led to the emergence of Explainable AI (XAI), which aims to enhance the trustworthiness and understandability of AI models by providing explanations for their decisions.

This thesis explores the concept of XAI and its implications for building interpretable AI models. The study reviews the existing literature on XAI and interpretable models, identifies key challenges and opportunities in the field, designs and implements an interpretable AI system, and evaluates its effectiveness in providing explanations for its decisions. By addressing these objectives, this research aims to contribute to the ongoing efforts to improve the transparency and accountability of AI systems across various domains.

In chapter two, a comprehensive literature review on XAI and interpretable models will be presented to provide a theoretical framework for the study. Chapter three will discuss the system design and methodology, including the selection of the interpretable model, the data preprocessing techniques, and the evaluation metrics used to assess the model’s performance. Chapter four will detail the system implementation, outlining the steps taken to build and test the interpretable AI system.

Finally, chapter five will provide a conclusion and summary of the project thesis, highlighting the key findings and implications of the study. Overall, this thesis aims to contribute to the ongoing discourse on XAI and interpretable models, shedding light on the importance of transparency and interpretability in AI systems for building trust and accountability in their decision-making processes.

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