Expanding Knowledge on ML : Explainable Models for Smarter Decisions

Abstract - In this paper, our objective is to shed light on previously unexplored and overlooked aspects of explainable machine learning. We aim to illustrate the diverse domains where machine learning has quietly become a transformative force. Through an extensive survey, we delve into existing research and illuminate the potential avenues yet to be explored. In an era marked by technological advancement, machine learning has seamlessly integrated into nearly every facet of global activities. To remain abreast of these developments, it is imperative to deepen our understanding of this field. Our goal is to immerse ourselves in the realm of machine learning, leveraging this knowledge to undertake a significant project. Within these pages, readers will find a comprehensive overview of our survey findings alongside insights into our own future endeavors in this dynamic field.

Index Terms - complexity, explain-ability, artificial intelligence, interpretability, interpretable, artificial intelligence, machine learning methods.

Introduction - This paper talks about, Explainable Machine Learning, which is an essential field in AI that focuses on deciphering complex machine learning models that aids in various driving the conclusion-making methods. Its objective is to provide understandable insights into how these models arrive at predictions or decisions, crucial for trust and accountability. XAI employs various techniques like feature importance analysis, Evaluating the impact of input characteristics on predictions is crucial in building robust, interpretable, and efficient machine learning models. Approaches like permutation advantages, SHAP or LIME help identify influential features, enhancing comprehension of model behaviour. Moreover, it promotes transparent model types (e.g., decision trees) whose structure is more interpretable than complex models like neural networks. The importance of XAI lies in fostering trust, ensuring regulatory compliance, identifying biases, and enabling experts to verify and enhance model performance. Its role is pivotal across sectors like healthcare, finance, and justice, ensuring responsible and ethical AI deployment in diverse applications.

Overview :

1. Provides a comprehensive survey of XAI methods, including SHAP, LIME, model-agnostic and model-specific techniques.
2. Discusses applications across key sectors such as healthcare, finance, cybersecurity, education, and civil engineering.
3. Highlights neural networks, their opacity, and how XAI enhances their interpretability.
4. Includes graphical analyses showcasing global AI adoption, job growth, and revenue investments.
5. Compares leading papers on XAI, summarizing advantages, limitations, and future directions.

Features :

1. Literature review of 50+ sources on XAI methods and real-world use-cases.
2. Categorization of interpretable vs. explainable ML models.
3. Visualizations of global machine learning trends and revenue comparisons.
4. Discussion on the future of XAI: ethical AI, transparency, and trust-building.
5. Table-based comparisons of key research works and unexplored areas.

Topics Covered :

1. Explainable AI (XAI)
2. Interpretable Machine Learning
3. SHAP & LIME
4. Knowledge Graphs in AI
5. Model transparency and auditability
6. Socio-ethical aspects of AI

Future Work :

1. Developing domain-specific XAI methods.
2. Enhancing non-technical stakeholder accessibility.
3. Integrating human-centric design into AI systems.
4. Building real-time explainable systems for critical sectors like healthcare and finance.