Vol. 8, Special Issue 2 (2019)

The paper begins by exploring the motivations behind the surge in interest in interpretable machine learning, elucidating the challenges posed by inherently opaque models. Subsequently, it provides an in-depth analysis of popular interpretable model techniques, ranging from traditional linear models to modern approaches like LIME (Local Interpretable Model-Agnostic Explanations) and SHAP (SHapley Additive ex Planations). By dissecting the strengths and limitations of each technique, the paper aims to empower practitioners and researchers to make informed choices based on their specific use cases and requirements.

Furthermore, the review delves into real-world applications where interpretable models play a pivotal role. Examples include healthcare diagnostics, where the interpretability of a model's decisions is crucial for gaining the trust of medical professionals and ensuring patient safety. Similarly, in the financial sector, interpretable models aid in risk assessment and regulatory compliance. The paper critically examines these applications, shedding light on instances where interpretable models offer tangible benefits over their opaque counterparts.

The review also addresses the ongoing challenges in the field, such as the interpretability-accuracy trade-off and the need for standardized evaluation metrics. It emphasizes the importance of developing universally accepted benchmarks to objectively assess the interpretability of different models. Moreover, the paper discusses emerging trends and future directions in interpretable machine learning, including the integration of domain knowledge and the incorporation of interpretability as an integral part of the model development process.