AAAS: Machine learning 'causing science crisis'
Curious (possibly naive) question: isn&#x27;t there a fundamental difference between the goals behind creating models with ML vs the &quot;old-fashioned&quot; way? That is, in modern ML applications, you&#x27;re creating a model with dozens&#x2F;hundreds of potential variables, without a hypothesis of how they relate or contribute to the target (other than that they might, hence your including them in the modeling process). You&#x27;re using the model for predictions more than for explainability (though there is work ongoing into improving explainability, but it seems kind of post hoc to me). And there&#x27;s an expectation that you will retrain, or at least tune, the model as its predictive accuracy decays over time.<p>By contrast, traditionally in science you&#x27;re coming in with a hypothesis ahead of time about what variables predict what target. The goal is to come up with a model that is consistent with your hypothesis (and possibly some existing theory), and which can be applied generally, and which should need no tuning. For example, the very simple model for Beer&#x27;s Law-- absorbance vs concentration. That is a law that will apply in every other circumstance, but if modern ML methods had been applied, the scientist might have chosen the model with a slightly better score but which includes nonsense variables in addition to concentration.<p>All that to say, it seems to me the problem stems from scientists&#x27; lack of hypotheses at the outset of a project, and&#x2F;or the understandable desire to get the best bang for their buck out of an experiment by measuring dozens of variables at once and hoping the magic of ML can find a hypothesis for them.<p>Hope that made sense.