Abstract
Demands on more transparency of the backbox nature of machine learning models have led to the recent rise of human-in-the-loop in machine learning, i.e. processes that integrate humans in the training and application of machine learning models. The present work argues that this process requirement does not represent an obstacle but an opportunity to optimize the design process. Hence, this work proposes a new process framework, Human-in-the-loop Design Cycles – a design process that integrates the structural elements of agile and design thinking process, and controls the training of a machine learning model by the human in the loop.
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