Disruptive Innovation, Predictive Analytics, and the Soft Determinism of the Fourth Industrial Revolution

• In Event: Disrupting, Broadcasting and Commercializing Science

Abstract

In this talk, we explore an aspect of the strategic deployment of “disruptive innovation” within the discourse of the so-called “fourth industrial revolution”. We offer a theoretical perspective on disruption as an ethos of industrial and commercial science and aim to contribute to STS by engaging with its implications for science and technology governance. One example of the potentially disruptive innovation concerns the impacts of artificial intelligence algorithms and predictive data analytics in judicial contexts. A.I. is increasingly used to perform non-routine cognitive tasks. Proprietary A.I. algorithms are used to make risk assessments in order to assist judges in sentencing convicted felons in some U.S. states. A.I. risk assessments and predictive data analytics (such as predicting the likelihood of recidivism) can operate in ways that disrupt or destabilize as well as to re-stabilize various conceptions of legal procedure amid concerns about bias in judicial decision-making. On various sides of controversies regarding the application of proprietary software in judicial contexts, conceptions of human rights, the practice of ethics, and legitimate distinctions between commercial and non-commercial applications of technology are fluid and contested. Underlying these destabilizations and re-stabilizations is a discourse engaged with the restructuring of science and technology governance. The ethos of disruption expresses a desire for entrepreneurs to deliver continuous innovation and also for the control of innovation though management and governance. We suggest that the ethos of disruption and its counterpart “agile” governance (hence “re-stabilization”) constitutes a kind of “soft determinism” and we explain how we conceive its significance within the context of a recent research project conducted at our home institution.

Authors

• Grant Fisher, Korea Advanced Institute of Science and Technology (KAIST)

• HyeJeong Han, Korea Advanced Institute of Science and Technology (KAIST)

• Richard Sung, Korea Advanced Institute of Science and Technology (KAIST)