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Preventing undesirable behavior of intelligent machines

Overview of attention for article published in Science, November 2019
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (99th percentile)
  • High Attention Score compared to outputs of the same age and source (96th percentile)

Mentioned by

news
29 news outlets
blogs
2 blogs
twitter
404 tweeters
facebook
1 Facebook page
wikipedia
1 Wikipedia page
reddit
1 Redditor

Citations

dimensions_citation
2 Dimensions

Readers on

mendeley
43 Mendeley
Title
Preventing undesirable behavior of intelligent machines
Published in
Science, November 2019
DOI 10.1126/science.aag3311
Pubmed ID
Authors

Philip S. Thomas, Bruno Castro da Silva, Andrew G. Barto, Stephen Giguere, Yuriy Brun, Emma Brunskill

Abstract

Intelligent machines using machine learning algorithms are ubiquitous, ranging from simple data analysis and pattern recognition tools to complex systems that achieve superhuman performance on various tasks. Ensuring that they do not exhibit undesirable behavior-that they do not, for example, cause harm to humans-is therefore a pressing problem. We propose a general and flexible framework for designing machine learning algorithms. This framework simplifies the problem of specifying and regulating undesirable behavior. To show the viability of this framework, we used it to create machine learning algorithms that precluded the dangerous behavior caused by standard machine learning algorithms in our experiments. Our framework for designing machine learning algorithms simplifies the safe and responsible application of machine learning.

Twitter Demographics

The data shown below were collected from the profiles of 404 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 43 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 43 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 13 30%
Researcher 10 23%
Student > Master 6 14%
Professor 4 9%
Professor > Associate Professor 3 7%
Other 7 16%
Readers by discipline Count As %
Computer Science 10 23%
Agricultural and Biological Sciences 5 12%
Medicine and Dentistry 3 7%
Psychology 3 7%
Chemistry 3 7%
Other 17 40%
Unknown 2 5%

Attention Score in Context

This research output has an Altmetric Attention Score of 569. 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 23 January 2020.
All research outputs
#14,303
of 14,382,991 outputs
Outputs from Science
#840
of 64,292 outputs
Outputs of similar age
#669
of 325,837 outputs
Outputs of similar age from Science
#29
of 823 outputs
Altmetric has tracked 14,382,991 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 99th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 64,292 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 46.3. This one has done particularly well, scoring higher than 98% 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 325,837 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 99% of its contemporaries.
We're also able to compare this research output to 823 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 96% of its contemporaries.