Explainable AI Bridging the Gap Between Machine Learning Models and Human Interpretability

Explainable AI (XAI) is an emerging field that seeks to bridge the gap between the transparent, at times impenetrable decision-making that is a natural consequence of machine learning (ML) models and human understanding. With artificial intelligence solutions gaining increasing prominence, especially in high-stakes markets like healthcare, finance, and law enforcement, the need for such models to be comprehensible, transparent, and trustworthy has become a major challenge. Despite significant advancements in AI technologies, the "black-box" nature of many models, especially those with deep learning and reinforcement learning, makes them unsuitable to implement in practical applications where human understanding and accountability are of utmost significance. This survey spans research contributions from 2015-2024, with significant advances in both model-agnostic methods, like LIME and SHAP, and methods towards constructing inherently interpretable models. While post-hoc interpretation methods provide useful information about feature importance, they do not necessarily provide rich causal explanations about the model's decision. Furthermore, there is always a trade-off between model performance and interpretability. Furthermore, XAI methods need to evolve to be capable of incorporating domain-specific needs, ethics, and fairness constraints so that AI systems are not just interpretable but also fair. The field of XAI research is pushing the scalability of interpretability techniques to work more effectively in real-time, high-dimensional, and dynamic settings. Future studies should also emphasize user-centric explainability, creating tools through which end-users may engage with models and comprehend decisions in terms of their own cognition. This overview points to the importance of taking a holistic approach that balances technical sophistication with user-centric and ethical principles, moving towards a more transparent and accountable AI future