# Explainable AI Frameworks: Navigating the Present Challenges and Unveiling Innovative Applications
## 1 Introduction
### Related Works  
The field of XAI has witnessed a surge in research, as evidenced by the numerous review studies (see Table 1). However, a gap exists in comprehensively analyzing the existing XAI frameworks. By identifying the limitations in the prior research, we aim to address these challenges and contribute to a deeper understanding of XAI frameworks. Our work focuses on reviewing the recent XAI papers and dissecting their contributions and limitations to pave the way for future advancements.  
\begin{table}
\begin{tabular}{p{42.7 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Paper** & **Year** & **Contribution** & **Limitation** \\ \hline
[4] & 2023 & The research highlights that current toolkits only cover a limited range of explanation quality aspects. This work guides researchers in selecting evaluation methods that offer a broader assessment of explanation quality. The research suggests cross-evaluation between toolkits/frameworks and original metric implementations to identify and address inconsistencies. \\ \hline
[1] & 2023 & The research provides a broad overview of various XAI techniques, encompassing white-box models (interpretable by design) and black-box models with techniques like LIMEs and SHAPs to enhance interpretability. The research highlights the need for a deeper understanding of XAI limitations and explores the importance of considering data explainability, model explainability, fairness, and accountability in tandem. It emphasizes the need for a holistic approach to XAI implementation and deployment. \\ \hline
[7] & 2022 & The chapter overviews available resources and identifies open problems in utilizing XAI techniques for user experience design. The chapter highlights the emergence of a research community dedicated to human-centered XAI, providing references for further exploration. \\ \hline
[8] & 2021 & The authors reviewed existing research and categorized various design goals and evaluation methods for XAI systems. To address the need for a comprehensive approach, the authors propose a new framework that connects design goals with evaluation methods. \\ \hline
[2] & 2021 & The research demonstrates how the framework can be used to compare various XAI methods and understand the aspects of explainability addressed by each. The framework situates explanations and interpretations within the bigger picture of an XAI pipeline, considering input/output domains and the gap between mathematical models and human understanding. \\ \hline
[6] & 2021 & The research acknowledges the growing emphasis on human stakeholders in XAI research and the need for evaluating explainability approaches based on their needs. The research emphasizes the importance of empirically evaluating explainability approaches to assess how well they meet stakeholder needs. \\ \hline
[9] & 2021 & The research proposes a classification framework for explanations considering six aspects: purpose, interpretation method, context, presentation format, stakeholder type, and domain. The research acknowledges the need to expand the framework by including more explanation styles and potentially sub-aspects like user models. \\ \hline \end{tabular}
\end{table}
Table 1: Summary of contributions and limitations of existing studies on XAI in the last five years.