The current practice of focusing the evaluation of a machine learning model on the accuracy of validation has been lately questioned, and has been declared as a systematic habit that is ignoring some important aspects when developing a possible solution to a problem. This lack of diversity in evaluation procedures reinforces the difference between human and machine perception on the relevance of data features, and reinforces the lack of alignment between the fidelity of current benchmarks and human-centered ...
The current practice of focusing the evaluation of a machine learning model on the accuracy of validation has been lately questioned, and has been declared as a systematic habit that is ignoring some important aspects when developing a possible solution to a problem. This lack of diversity in evaluation procedures reinforces the difference between human and machine perception on the relevance of data features, and reinforces the lack of alignment between the fidelity of current benchmarks and human-centered tasks. Hence, we argue that there is an urgent need to start paying more attention to the search for metrics that, given a task, take into account the most humanly relevant aspects. We propose to base this search on the errors made by the machine and the consequent risks involved in moving human logic away from that of the machine. If we work on identifying these errors and organize them hierarchically according to this logic, we can use this information to provide a reliable evaluation of machine learning models, and improve the alignment between training processes and the different considerations humans make when solving a problem and analyzing outcomes. In this context we define the concept of non-human errors, exemplifying it with an image classification task and discussing its implications.
+
