Towards expert–machine collaborations for technology valuation: An interpretable machine learning approach

Abstract

Although technology valuation has benefited considerably from recent advances in machine learning technology, the results of prior studies in this field are of limited use in practice because they rely solely on black box models whose internal mechanisms are hidden. We develop an analytical framework for successful expert–machine collaborations for technology valuation using interpretable machine learning that makes a model's behaviors and predictions understandable to humans. First, a technological characteristics–economic value matrix is constructed using patent and technology transaction databases. Second, machine learning models are developed to examine the nonlinear and complex relationships between the technological characteristics and economic value of technologies. Third, the performance of the machine learning models is assessed using quantitative metrics. Finally, the SHapley Additive exPlanation method is applied to the best-performing model to explain which technological characteristics influence the economic value of technologies. By these means, we investigate the importance of the features of technological characteristics (and their interactions) in technology valuation and offer theoretical and practical implications of the analysis results. A case study of the technologies registered in the Office of Technology Licensing at Stanford University confirms that our framework is a useful complementary tool for technology valuation.