Interpretable Machine Learning (IML) focuses on creating machine learning models whose decisions can be understood and trusted by humans. As machine learning models become increasingly complex and are deployed in high-stakes applications such as healthcare, finance, and autonomous systems, interpretability is crucial to ensure transparency, fairness, and reliability.Interpretable Machine Learning is a rapidly growing field that bridges the gap between high-performance machine learning models and human understanding. The above PhD project ideas span a wide range of topics, from enhancing existing interpretability methods to exploring new areas like fairness and human-centered interpretability. These projects have the potential to contribute significantly to the development of more transparent and trustworthy AI systems across a variety of critical applications.