Explainable AI (XAI) is a branch of AI research that focuses on developing models and techniques that can provide human-understandable explanations for their predictions or decision making processes. However, there are several limitations and challenges that can impede the delivery of truly explainable AI systems.
One major limitation of XAI is that it is often difficult to define what exactly constitutes an “explanation” that is understandable to humans. While some researchers have proposed specific metrics to evaluate the explainability of a model, there is no consensus on what constitutes a satisfactory explanation.
Another limitation is that many current XAI methods only provide “post-hoc” explanations, which are generated after a model has made a prediction. These explanations may not be truly representative of the model’s internal processes, and they may also be subject to bias or manipulation.
Additionally, some have argued that truly explainable AI systems may be less accurate or less performant than their non-explainable counterparts. This is because the process of making a model explainable may require simplifying or distilling the model’s internal representations, which can lead to a loss of information or accuracy.
Furthermore, the expectations from explainable AI are high, but the current state of the technology is still at a stage where they are not able to deliver completely transparent and verifiable explanations, there are several approaches that have been proposed, but it is still an active area of research.
Another thing to consider is that even if we can build a model that is completely transparent and verifiable, it doesn’t guarantee that the end user will understand the explanation, lack of domain knowledge can also hinder explainability.
In conclusion, while the idea of explainable AI is appealing, there are many limitations and challenges that currently impede its delivery. These include difficulties in defining what constitutes an “explanation”, limitations of post-hoc explanations, trade-offs between explainability and accuracy, and end user’s ability to understand the explanation. However, active research in the field is ongoing and it can be expected that the current limitations will be addressed and overcome in the future.