# Making Sense of Machine Learning: A Review of Interpretation Techniques and Their Applications
## 7 Challenges and Limitations  
The human-centered approach to XAI takes into account the value that users will ultimately derive from the models. The complexity of these models can often be such that humans struggle to understand them, but more interpretable models may not perform as well--in general, there is a trade-off between accuracy and interpretability. If decision-makers cannot understand their models, they may not trust them, which can lead to a lack of adoption and decrease the ultimate effectiveness of the AI system. Many multiple criteria decision making methods have assumed a fixed importance weighting for each criterion. The ability of LIMEs to discover the most important variables is questionable when the importance of each factor varies with context, or is even thresholded in a rules-based model (whether explicitly or implicitly). One approach to address this challenge is shown by [PERSON] [88], who introduced the notion of contextual importance and utility (CIU), building on past research on the theory of how humans actually reach decisions. The notion here is that the importance to a human of an input may vary given the context. Several challenges still remain to be addressed to ensure that XAI systems can be deployed efficiently, safely and ethically. These challenges can be categorized into four main areas, namely technical challenges, ethical challenges, social challenges, and regulatory challenges. One of the technical challenges of XAI is that many of the techniques developed for explaining models have been designed for specific types of models. For example, post hoc techniques such as LIMEs and SHAPs are best suited for models that are based on feature importance, whereas counterfactual explanations are better suited to rule-based systems. [PERSON] [89] argues that there is a need to move away from black box models towards models that are inherently interpretable. The use of interpretable models can provide a more transparent and understandable decision-making process. One of the ethical challenges of XAI is that it can be difficult to determine what level of explanation is sufficient for different scenarios. [PERSON] et al. [3] discuss the need to ensure that the explanations provided by XAI systems are appropriate for the context in which they are deployed. The explanations must be able to convey the necessary information to users without overwhelming them with too much technical detail. Furthermore, the use of XAI must not perpetuate existing biases or discrimination in society. Social challenges of XAI include issues related to human trust and perception. [PERSON] [90] investigated human trust and perception of AI systems. Their study showed that participants were more likely to trust and accept the decisions of systems that provided explanations for their outputs. However, the study also showed that the level of trust and acceptance varied depending on the type of explanation provided. The regulatory challenges of XAI include issues related to the legal and ethical implications of AI systems. [PERSON] et al. [3,91] discuss the need for clear regulationsand standards for the use of XAI in different domains. The regulations must be able to provide guidance on the ethical and legal implications of AI systems, as well as the potential risks associated with their use. [PERSON] et al. [92] discuss the need to ensure that AI systems are held accountable for their decisions. They propose a framework for the design of XAI systems that includes mechanisms for detecting and reporting errors in the decision-making process. The challenges associated with XAI are varied and require a multidisciplinary approach to address them. Technical challenges require the development of techniques that are applicable across different types of models. Ethical challenges require the development of systems that are transparent, fair, and do not perpetuate existing biases in society. Social challenges require the development of systems that are trusted and perceived as fair by humans.