From the linked paper, linked again [0] below, I think this represents Rudins philosophical view, and why it could be practical: Here is the Rashomon set arg

From the linked paper, linked again [0] below, I think this represents Rudins philosophical view, and why it could be practical: Here is the Rashomon set argument: Consider that the data permit a large set of reasonably accurate predictive models to exist. Because this set of accurate models is large, it often contains at least one model that is interpretable. This model is thus both interpretable and accurate. Unpacking this argument slightly, for a given data set, we define the Rashomon set as the set of reasonably accurate predictive models (say within a given accuracy from the best model accuracy of boosted decision trees). Because the data are finite, the data could admit many close-to-optimal models that predict differently from each other: a large Rashomon set. I suspect this happens often in practice because sometimes many different machine learning algorithms perform similarly on the same dataset, despite having different functional forms (e.g., random forests, neural networks, support vector machines). As long as the Rashomon set contains a large enough set of models with diverse predictions, it probably contains functions that can be approximated well by simpler functions, and so the Rashomon set can also contain these simpler functions. Said another way, uncertainty arising from the data leads to a Rashomon set, a larger Rashomon set probably contains interpretable models, thus interpretable accurate models often exist. If this theory holds, we should expect to see interpretable models exist across domains. These interpretable models may be hard to find through optimization, but at least there is a reason we might expect that such models exist. [0] https://arxiv.org/pdf/1811.10154.pdf https://arxiv.org/pdf/1811.10154.pdf