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Critiquing the Reasons for Making Artificial Moral Agents

Overview of attention for article published in Science and Engineering Ethics, February 2018
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#30 of 975)
  • High Attention Score compared to outputs of the same age (95th percentile)
  • High Attention Score compared to outputs of the same age and source (95th percentile)

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Title
Critiquing the Reasons for Making Artificial Moral Agents
Published in
Science and Engineering Ethics, February 2018
DOI 10.1007/s11948-018-0030-8
Pubmed ID
Authors

Aimee van Wynsberghe, Scott Robbins

Abstract

Many industry leaders and academics from the field of machine ethics would have us believe that the inevitability of robots coming to have a larger role in our lives demands that robots be endowed with moral reasoning capabilities. Robots endowed in this way may be referred to as artificial moral agents (AMA). Reasons often given for developing AMAs are: the prevention of harm, the necessity for public trust, the prevention of immoral use, such machines are better moral reasoners than humans, and building these machines would lead to a better understanding of human morality. Although some scholars have challenged the very initiative to develop AMAs, what is currently missing from the debate is a closer examination of the reasons offered by machine ethicists to justify the development of AMAs. This closer examination is especially needed because of the amount of funding currently being allocated to the development of AMAs (from funders like Elon Musk) coupled with the amount of attention researchers and industry leaders receive in the media for their efforts in this direction. The stakes in this debate are high because moral robots would make demands on society; answers to a host of pending questions about what counts as an AMA and whether they are morally responsible for their behavior or not. This paper shifts the burden of proof back to the machine ethicists demanding that they give good reasons to build AMAs. The paper argues that until this is done, the development of commercially available AMAs should not proceed further.

X Demographics

X Demographics

The data shown below were collected from the profiles of 104 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 227 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 227 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 32 14%
Student > Ph. D. Student 29 13%
Student > Bachelor 28 12%
Researcher 26 11%
Student > Doctoral Student 13 6%
Other 32 14%
Unknown 67 30%
Readers by discipline Count As %
Computer Science 42 19%
Social Sciences 28 12%
Philosophy 23 10%
Engineering 14 6%
Business, Management and Accounting 10 4%
Other 39 17%
Unknown 71 31%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 70. 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 14 February 2022.
All research outputs
#622,717
of 25,760,414 outputs
Outputs from Science and Engineering Ethics
#30
of 975 outputs
Outputs of similar age
#14,092
of 345,361 outputs
Outputs of similar age from Science and Engineering Ethics
#1
of 23 outputs
Altmetric has tracked 25,760,414 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 97th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 975 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 13.0. This one has done particularly well, scoring higher than 96% 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 345,361 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 95% of its contemporaries.
We're also able to compare this research output to 23 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 95% of its contemporaries.