# Making Sense of Machine Learning: A Review of Interpretation Techniques and Their Applications
## 8 Conclusions and Future Research Directions  
With the increasing use of ML models in various domains, there is a growing need for these models to be transparent, interpretable, and accountable. However, the complexity of deep learning and other modern AI models makes generating interpretable explanations challenging and there is in general a trade-off between accuracy and explainability. This highlights the importance of XAI in providing tools to ensure AI system outputs can be understood by human users, enhancing trust, adoption, and ultimate effectiveness of these systems. This can also have a positive impact on regulatory compliance and the ethical use of AI in various fields. This review provides a comprehensive overview of foundational XAI techniques within the AI domain, including model-agnostic methods, post hoc explanations such as LIMEs and SHAPs, counterfactual explanations, and intrinsically interpretable models. By addressing critical questions ranging from the evolution of interpretability methodologies to the impact of diverse interpretation techniques on fostering human-AI collaboration and regulatory compliance, this review has shed light on the intricate facets of explainability in AI models. Through an exploration of the strengths and limitations associated with the application of these techniques across varied data domains, including images, text, and tabular data, this review has emphasized the nuanced challenges and opportunities prevalent in real-world applications. Furthermore, by examining the transformative influence of advanced computational methods on the dynamic landscape of AI interpretability, this review has revealed the evolving trends that shape the future trajectory of this critical field. As a result, this comprehensive analysis serves as a resource for individuals seeking a holistic understanding of the complexities and implications inherent in the realm of interpretability in AI systems. Furthermore, we highlighted the importance of XAI in various fields such as healthcare, finance and law enforcement and highlighted some of the challenges associated with the development and deployment of XAI systems, including technical, ethical, social, and regulatory challenges. These challenges can also be seen as opportunities for further research and development. It appears that, while deep learning has shown considerable advantages for \"unstructured\" data, like images and text, for structured data which comes as a table of numbers say, deep learning is far from the best approach at the moment [93]. What are the implications of this? (1) Massive search and optimization may produce accurate models but they may not be interpretable. However, problems that have a large set of possible solutions (i.e., a large Rashomon set) may include models that are simple, interpretable, robust and accurate. [PERSON] et al. [89,94] notably advocates this approach and has shown in a number of cases that large black box models can be replaced by remarkably simple and interpretable models with no lack of accuracy. They outline a number of challenges for interpretable ML [95]; (2) modern deep learning architectures have achieved surprising accuracy while appearing to be over-parameterized and un-regularized. [PERSON] et al. [96] showed that, while classification accuracy is very high, many of these approaches cause the model to be very poorly calibrated. This may be an important point for some use cases where we want the outputs to be accurate estimates of probabilities. The current limitations of deep learning in handling unstructured data have been noted. However, research in the area of interpretable ML has shown that simple, interpretable models can provide accurate results. Finally, we should consider the importance of model calibration, especially in cases where accurate probability estimates are needed.