Technological Decision Making: Fused Shale Tool Production in California

2011 ◽  
Vol 32 (1) ◽  
pp. 15-48 ◽  
Author(s):  
Jeanne E. Arnold
Keyword(s):  
Decision Making ◽  
Technological Decision Making

Two Case Studies Illustrating Aspects of Technological Decision-Making

1983 ◽  
Vol 3 (2) ◽  
pp. 166-169
Author(s):  
Ernest Braun ◽  
David Collingridge ◽  
Kate Hinton
Keyword(s):  
Decision Making ◽  
Case Studies ◽  
Technological Decision Making

Challenges to the Story of Innovation

2020 ◽  
pp. 23-34
Author(s):  
Michiel van Oudheusden
Keyword(s):  
Decision Making ◽  
Political Economy ◽  
Science And Technology Studies ◽  
Science And Technology ◽  
Responsible Research And Innovation ◽  
The Political ◽  
The Public ◽  
Research And Innovation ◽  
Responsible Research ◽  
Technological Decision Making

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.

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

2020 ◽  
pp. 1-21
Author(s):  
Justin B. Biddle
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Value Judgments ◽  
Epistemic Risk ◽  
Prediction Algorithms ◽  
Recent Scholarship ◽  
Recidivism Prediction ◽  
Human Decision ◽  
Technological Decision Making

Abstract 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.

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