Position: Why We Must Rethink Empirical Research in Machine Learning

In practice, that leads to non-replicable results, makes it may jeopardize applied empirical researchers' confidence findings unreliable, and threatens to undermine in experimental results and discourage them from applying progress in the field. To overcome this alarming ML methods, even though these novel approaches might be situation, we call for more awareness of the beneficial. For example, ML is increasingly being used in plurality of ways of gaining knowledge experimentally the medical domain, and this is often promising in terms of but also of some epistemic limitations.