## 6 Challenges of XAI Approaches
### Controversy around Current XAI Methods  
Currently, one of the frequently discussed issues among researchers in the field of XAI is the choice between different approaches, mainly between post hoc explainability and other model-dependent criteria. There is no consensus about whether it is better to use intrinsically interpretable models or to develop techniques that try to explain the outcomes of black-box models, given the contrasts and complementarities between them that make comparisons incommensurate.  
Despite being more understandable (and therefore meeting some of the explainability needs that make other types of systems more problematic), interpretable models are commonly rated as less accurate or efficient than more complex data-based approaches, such as DL (see Figure 5), implying that there is a trade-off between explainability and classification or prediction accuracy [11; 30; 123] (RQ2).  
If such a trade-off exists, it could be alleged that it would not be ethical to use a model that does not perform at the best possible level, therefore being more adequate to use top-notch accurate systems, regardless of their black-box nature [8]. Some researchers maintain that, in fact, explainability might not be so necessary if good accuracy is proven by conducting empirical validations, for example [124; 125], while others disagree [126; 127]. The practical ethical problem is the heterogeneity and lack of \"gold standards\" of comparable training and testing datasets for any particular problem with statistical and heuristic approaches, which do not take into account the wide range of qualitative differences between different types of data and knowledge, and the implementation of responsible evaluatory judgments by experts which cannot be reduced to simple measures. Complementary ap  
Figure 5: Trade-off between explainability and performance of different AI methods. Adapted from reference [67].  
proaches for this might be official certifications or criteria for controlling data biases, beside explainability, in order to build trust on these systems [128; 129].  
Moreover, some scholars argue that, while it is important that research continues trying to unravel black-box models, we should also focus on pursuing more efficient and stable models, and putting so much emphasis on the interpretability of these models before clinical application might restrain innovation in that direction and might not be that crucial [130].  
However, from an opposite point of view, other researchers advocate for intrinsically interpretable models, arguing that there is no such trade-off between explainability and accuracy in reality and that these models are more appropriate and safe for high-stakes fields like healthcare, where explanations of black-box models can be misleading and inaccurate, inducing the opposite effects to those intended [126]. At the same time, some interpretable models have been recognised to require further explanations for non-technical end users such as clinicians or patients [31].  
While there is no clear agreement on what is the best approach to the problem of transparency and interpretability or if it is even always necessary, some studies show that physicians, unsurprisingly, prefer AI systems that include some form of explainability, while seeming not to have a clear preference between methods [7].  
In the following section, we will discuss further limitations of the explanations related to this controversy, and that should be taken into account when designing these kinds of models and choosing among explainability approaches.