Don't Teach Machine Learning! Teach Data Science!
fliptop writes:
Over at ACM.org, Orit Hazzan (a professor in the Technion's Department of Education in Science and Technology) and Koby Mike (a Ph.D student) make the case that machine learning guides learners to ignore the application domain even when it is relevant for the modeling phase of data science:
From a historical perspective, machine learning was considered, for the past 50 years or so, as part of artificial intelligence. It was taught mainly in computer science departments to scientists and engineers and the focus was placed, accordingly, on the mathematical and algorithmic aspects of machine learning, regardless of the application domain. Thus, although machine learning deals also with statistics, which focuses on data and does consider the application domain, up until recently, most machine learning activities took place in the context of computer science, where it began, and which focuses traditionally on algorithms.
Two processes, however, have taken place in parallel to the accelerated growth of data science in the last decade. First, machine learning, as a sub-field of data science, flourished and its implementation and use in a variety of disciplines began. As a result, researchers realized that the application domain cannot be neglected and that it should be considered in any data science problem-solving situation. For example, it is essential to know the meaning of the data in the context of the application domain to prepare the data for the training phase and to evaluate the algorithm's performance based on the meaning of the results in the real world. Second, a variety of population began taking machine learning courses, people for whom, as experts in their disciplines, it is inherent and essential to consider the application domain in data science problem-solving processes.
[...] For example, consider a researcher in the discipline of social work who took a machine learning course but was not educated to consider the application domain in the interpretation of the data analysis. The researcher is now asked to recommend an intervention program. Since the researcher was not educated to consider the application domain, he or she may ignore crucial factors in this examination and rely only on the recommendation of the machine learning algorithm.
Other examples are education and transportation, fields that everyone feels they understand. As a result of a machine learning education that does not consider the application domain, non-experts in these fields may assume that they have enough knowledge in these fields, and may not understand the crucial role that professional knowledge in these fields plays in decision-making processes that are based on the examination of the output of machine learning algorithms. This phenomenon is further highlighted when medical doctors or food engineers, for example, are not trained or educated in machine learning courses to criticize the results of machine learning algorithms based on their professionalism in medicine and food engineering, respectively.
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