Interpretability is a major concern when it comes to artificial intelligence (AI) models. Since these models are often opaque, it can be difficult to understand how or why they produce the results they do. This lack of interpretability can be a barrier to adoption of AI technology, since stakeholders may not be comfortable using a system if they cannot understand how it works. Additionally, it can make it difficult to debug and improve AI models, since it is hard to identify where errors are coming from if the mechanisms underlying the model are unclear.
There are a number of ways to make AI models more interpretable. One approach is to use techniques like feature extraction and feature selection to identify which input features are most important to the model’s predictions. This can help to provide a qualitative understanding of how the model is working. Additionally, there are a number of statistical and visualization methods that can be used to examine the output of AI models and understand which inputs are most important to their predictions.
Another approach to increasing the interpretability of AI models is to use so-called “interpretable” or “transparent” models. These are models that have been specifically designed to be easy to understand, typically by sacrificing some accuracy in order to make the model’s workings more understandable. Many of these interpretable models are based on decision trees, which provide a clear and concise explanation of the model’s predictions.
The trade-off between interpretability and accuracy is an important one to consider when designing AI systems. In some cases, it may be more important to have a model that is easy to understand and debug, even if it is not as accurate as a more opaque model. In other cases, the increased accuracy of a more complex model may be worth the trade-off in interpretability. Ultimately, the decision of whether to use an interpretable or opaque model will depend on the specific needs of the application.
References:
https://www.britannica.com/technology/artificial-intelligence
https://en.wikipedia.org/wiki/Interpretability
https://machinelearningmastery.com/feature-extraction-machine-learning-python/
https://towardsdatascience.com/transparency-and-interpretability-in-machine-learning-ddd11b35WB2