Human-in-the-Loop Design Cycles -- A Process Framework that Integrates Design Sprints, Agile Processes, and Machine Learning with Humans

• URL: http://arxiv.org/abs/2003.05268v1
• Date: Sat, 29 Feb 2020 07:35:35 GMT
• Title: Human-in-the-Loop Design Cycles -- A Process Framework that Integrates Design Sprints, Agile Processes, and Machine Learning with Humans
• Authors: Chaehan So
• Abstract summary: This work proposes a new process framework, Human-in-the-learning-loop (HILL) Design Cycles. The HILL Design Cycles process replaces the qualitative user testing by a quantitative psychometric measurement instrument for design perception. The generated user feedback serves to train a machine learning model and to instruct the subsequent design cycle along four design dimensions.
• Score: 1.52292571922932
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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-learning-loop (HILL) 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. The HILL Design Cycles process replaces the qualitative user testing by a quantitative psychometric measurement instrument for design perception. The generated user feedback serves to train a machine learning model and to instruct the subsequent design cycle along four design dimensions (novelty, energy, simplicity, tool). Mapping the four-dimensional user feedback into user stories and priorities, the design sprint thus transforms the user feedback directly into the implementation process. The human in the loop is a quality engineer who scrutinizes the collected user feedback to prevents invalid data to enter machine learning model training.

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