Inductive Risk, Science, and Values: A Reply to MacGillivray

Risk Analysis ◽  
2019 ◽  
Vol 40 (4) ◽  
pp. 667-673
Author(s):  
Daniel J. Hicks ◽  
P. D. Magnus ◽  
Jessey Wright
Keyword(s):  
Inductive Risk ◽  
Science And Values

scholarly journals Human Induction in Machine Learning

2021 ◽  
Vol 54 (3) ◽  
pp. 1-18
Author(s):  
Petr Spelda ◽  
Vit Stritecky
Keyword(s):  
Machine Learning ◽  
A Posteriori ◽  
Experimental Paradigm ◽  
Risk Minimisation ◽  
Inductive Risk ◽  
Acceptable Accuracy ◽  
Space And Time ◽  
The Common ◽  
Testing Set

As our epistemic ambitions grow, the common and scientific endeavours are becoming increasingly dependent on Machine Learning (ML). The field rests on a single experimental paradigm, which consists of splitting the available data into a training and testing set and using the latter to measure how well the trained ML model generalises to unseen samples. If the model reaches acceptable accuracy, then an a posteriori contract comes into effect between humans and the model, supposedly allowing its deployment to target environments. Yet the latter part of the contract depends on human inductive predictions or generalisations, which infer a uniformity between the trained ML model and the targets. The article asks how we justify the contract between human and machine learning. It is argued that the justification becomes a pressing issue when we use ML to reach “elsewhere” in space and time or deploy ML models in non-benign environments. The article argues that the only viable version of the contract can be based on optimality (instead of on reliability, which cannot be justified without circularity) and aligns this position with Schurz's optimality justification. It is shown that when dealing with inaccessible/unstable ground-truths (“elsewhere” and non-benign targets), the optimality justification undergoes a slight change, which should reflect critically on our epistemic ambitions. Therefore, the study of ML robustness should involve not only heuristics that lead to acceptable accuracies on testing sets. The justification of human inductive predictions or generalisations about the uniformity between ML models and targets should be included as well. Without it, the assumptions about inductive risk minimisation in ML are not addressed in full.

Science and Values Influencing Predator Control for Alaska Moose Management

2010 ◽  
Vol 74 (5) ◽  
pp. 917-928 ◽  
Author(s):  
Rodney D. Boertje ◽  
Mark A. Keech ◽  
Thomas F. Paragi
Keyword(s):  
Predator Control ◽  
Science And Values

Birth Control: Science and Values

Science ◽  
1964 ◽  
Vol 144 (3624) ◽  
pp. 1293-1294
Author(s):  
Roman A. Schmitt
Keyword(s):  
Birth Control ◽  
Science And Values

Knowledge, Science, and Values

2000 ◽  
Author(s):  
Tadeusz Czezowski
Keyword(s):  
Science And Values

Thoreau and Leopold on science and values

1994 ◽  
pp. 31-46 ◽  
Author(s):  
Bryan G. Norton
Keyword(s):  
Science And Values

scholarly journals Inductive risk and the contexts of communication

Synthese ◽  
2014 ◽  
Vol 192 (1) ◽  
pp. 79-96 ◽  
Author(s):  
Stephen John
Keyword(s):  
Inductive Risk

scholarly journals Love Slaves and Wonder Women: Radical Feminism and Social Reform in the Psychology of William Moulton Marston

2016 ◽  
Vol 2 (1) ◽  
Author(s):  
Matthew J Brown
Keyword(s):  
Emotional Health ◽  
Comic Book ◽  
Scientific Work ◽  
Psychological Research ◽  
Individual Values ◽  
Basic Emotions ◽  
Radical Feminist ◽  
Wonder Woman ◽  
Science And Values ◽  
History Of

In contemporary histories of psychology, William Moulton Marston is remembered for helping develop the lie detector test. He is better remembered in the history of popular culture for creating the comic book superhero Wonder Woman. In his time, however, he contributed to psychological research in deception, basic emotions, abnormal psychology, sexuality, and consciousness. He was also a radical feminist with connections to women's rights movements. Marston's work is an instructive case for philosophers of science on the relation between science and values. Although Marston's case provides further evidence of the role that feminist values can play in scientific work, it also poses challenges to philosophical accounts of value-laden science. Marston's work exemplifies standard views about feminist value-laden research in that his feminist values help him both to criticize the research of others and create novel psychological concepts and research techniques. His scientific work includes an account of the nature of psycho-emotional health that leads to normative conclusions for individual values and conduct and for society and culture, a direction of influence that is relatively under-theorized in the literature. To understand and evaluate Marston's work requires an approach that treats science and values as mutually influencing; it also requires that we understand the relationship between science advising and political advocacy in value-laden science.

Between Science and Values. Loren R. Graham

Isis ◽  
1984 ◽  
Vol 75 (3) ◽  
pp. 571-572
Author(s):  
Peter Weingart
Keyword(s):  
Science And Values
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