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When Technologies Makes Good People Do Bad Things: Another Argument Against the Value-Neutrality of Technologies

Overview of attention for article published in Science and Engineering Ethics, August 2013
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (89th percentile)
  • High Attention Score compared to outputs of the same age and source (84th percentile)

Mentioned by

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1 news outlet
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4 X users

Citations

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16 Dimensions

Readers on

mendeley
47 Mendeley
Title
When Technologies Makes Good People Do Bad Things: Another Argument Against the Value-Neutrality of Technologies
Published in
Science and Engineering Ethics, August 2013
DOI 10.1007/s11948-013-9464-1
Pubmed ID
Authors

David R. Morrow

Abstract

Although many scientists and engineers insist that technologies are value-neutral, philosophers of technology have long argued that they are wrong. In this paper, I introduce a new argument against the claim that technologies are value-neutral. This argument complements and extends, rather than replaces, existing arguments against value-neutrality. I formulate the Value-Neutrality Thesis, roughly, as the claim that a technological innovation can have bad effects, on balance, only if its users have "vicious" or condemnable preferences. After sketching a microeconomic model for explaining or predicting a technology's impact on individuals' behavior, I argue that a particular technological innovation can create or exacerbate collective action problems, even in the absence of vicious preferences. Technologies do this by increasing the net utility of refusing to cooperate. I also argue that a particular technological innovation can induce short-sighted behavior because of humans' tendency to discount future benefits too steeply. I suggest some possible extensions of my microeconomic model of technological impacts. These extensions would enable philosophers of technology to consider agents with mixed motives-i.e., agents who harbor some vicious preferences but also some aversion to acting on them-and to apply the model to questions about the professional responsibilities of engineers, scientists, and other inventors.

X Demographics

X Demographics

The data shown below were collected from the profiles of 4 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 47 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 1 2%
Unknown 46 98%

Demographic breakdown

Readers by professional status Count As %
Student > Master 11 23%
Student > Bachelor 6 13%
Professor > Associate Professor 4 9%
Student > Ph. D. Student 4 9%
Other 2 4%
Other 9 19%
Unknown 11 23%
Readers by discipline Count As %
Social Sciences 6 13%
Business, Management and Accounting 5 11%
Arts and Humanities 5 11%
Engineering 4 9%
Philosophy 3 6%
Other 13 28%
Unknown 11 23%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 14. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 30 April 2023.
All research outputs
#2,390,417
of 24,698,221 outputs
Outputs from Science and Engineering Ethics
#197
of 950 outputs
Outputs of similar age
#20,332
of 204,940 outputs
Outputs of similar age from Science and Engineering Ethics
#3
of 13 outputs
Altmetric has tracked 24,698,221 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 90th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 950 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 12.3. This one has done well, scoring higher than 79% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 204,940 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 89% of its contemporaries.
We're also able to compare this research output to 13 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 84% of its contemporaries.