| Title | Explainable Artificial Intelligence (XAI): Enhancing Transparency and Trust in Machine Learning Systems |
| Research Area | Machine Learning |
| Abstract | Artificial Intelligence (AI) systems are increasingly used in critical domains such as healthcare, finance, autonomous vehicles, and legal decision-making. While these systems offer high accuracy and automation capabilities, many advanced models—especially deep learning networks—operate as “black boxes,” making their decisions difficult to interpret. This lack of transparency raises concerns regarding trust, accountability, fairness, and regulatory compliance. Explainable Artificial Intelligence (XAI) has emerged as a research field focused on developing models and techniques that provide understandable explanations for AI decisions. This paper presents a comprehensive study of XAI, including its importance, techniques, evaluation methods, and real-world applications. The study demonstrates that XAI enhances user trust, supports decision validation, and ensures ethical AI deployment. Challenges such as trade-offs between accuracy and interpretability are also discussed, along with future research directions. |
| Keywords | Explainable AI, XAI, Model Interpretability, Transparency, Machine Learning, Ethical AI |
| Paper Status | Published |
| Volume | 2 |
| Issue | 2 |
| Published On | 19/03/2026 |
| Published File |
IJSRTD_4877.pdf
|