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Recent advancements in machine learning research, i.e., deep learning,introduced methods that excel conventional algorithms as well as humans inseveral complex tasks, ranging from detection of objects in images and speechrecognition to playing difficult strategic games. However, the currentmethodology of machine learning research and consequently, implementations ofthe real-world applications of such algorithms, seems to have a recurringHARKing (Hypothesizing After the Results are Known) issue. In this work, weelaborate on the algorithmic, economic and social reasons and consequences ofthis phenomenon. We present examples from current common practices ofconducting machine learning research (e.g. avoidance of reporting negativeresults) and failure of generalization ability of the proposed algorithms anddatasets in actual real-life usage. Furthermore, a potential future trajectoryof machine learning research and development from the perspective ofaccountable, unbiased, ethical and privacy-aware algorithmic decision making isdiscussed. We would like to emphasize that with this discussion we neitherclaim to provide an exhaustive argumentation nor blame any specific institutionor individual on the raised issues. This is simply a discussion put forth byus, insiders of the machine learning field, reflecting on us.

HARK Side of Deep Learning -- From Grad Student Descent to Automated Machine Learning