Evaluating design ideas is necessary to predict their success and assess their impact early on in the process. Existing methods rely either on metrics computed by systems that are effective but subject to errors and bias, or experts’ ratings, which are accurate but expensive and long to collect. Crowdsourcing offers a compelling way to evaluate a large number of design ideas in a short amount of time while being cost-effective. Workers’ evaluation is, however, less reliable and might substantially differ from experts’ evaluation.
In this work, we investigate workers’ rating behavior and compare it with experts. First, we instrument a crowdsourcing study where we asked workers to evaluate design ideas from three innovation challenges. We show that workers share similar insights with experts but tend to rate more generously and weigh certain criteria more importantly. Next, we develop a hybrid human-AI approach that …
