On Predicting Recidivism: Epistemic Risk, Tradeoffs, and Values in Machine Learning

Recent scholarship in philosophy of science and technology has shown that scientific and technological decision making are laden with values, including values of a social, political, and/or ethical character. This paper examines the role of value judgments in the design of machine-learning (ML) systems generally and in recidivism-prediction algorithms specifically. Drawing on work on inductive and epistemic risk, the paper argues that ML systems are value laden in ways similar to human decision making, because the development and design of ML systems requires human decisions that involve tradeoffs that reflect values. In many cases, these decisions have significant—and, in some cases, disparate—downstream impacts on human lives. After examining an influential court decision regarding the use of proprietary recidivism-prediction algorithms in criminal sentencing, Wisconsin v. Loomis, the paper provides three recommendations for the use of ML in penal systems.

Challenges to the Story of Innovation

This chapter sets out the meanings attached to the concept of ‘innovation’ and asks how it has recently come to occupy the political and economic position it now holds. Drawing from science and technology studies, which has long sought to better incorporate the public in technological decision-making, it explores the impetus towards connecting ‘responsibility’ with ‘innovation’ and the context from which this derives. Finally, it examines how this impetus has become incorporated into various frameworks for Responsible (Research and) Innovation, and what is missing from this approach in terms of understanding the place of ‘innovation’ in the present political economy, and the place of politics in innovation.

Addressing Sustainable Development: Promoting Active Informed Citizenry through Trans-Contextual Science Education

This article seeks to identify the role of science education in promoting an active, scientifically literate, citizenry ready to address sustainable development goals as envisaged by the United Nations (2015). In so doing, a conceptual model is put forward to address citizenry development, extending beyond an informed scientific and technological decision making ability and encompassing constructive activities addressing sustainable development at the local, national and global level. The operationalisation of the model builds on an initial student-relevant, societal issue-related contextualisation involving STEM (science, technology, engineering, mathematics) while focusing on developing science conceptual learning. The model extends to not only considering socio-scientific issues, but seeks to promote trans-contextualisation beyond the school setting, seeking to raise awareness of an active informed citizenry, related to environmental, economic and social sustainability. The components of active informed citizenry are described and a trans-contextual science teaching example based on the model is put forward in this article.