↓ Skip to main content

Fair Prediction with Disparate Impact: A Study of Bias in Recidivism Prediction Instruments

Overview of attention for article published in Big Data, June 2017
Altmetric Badge

About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#14 of 304)
  • High Attention Score compared to outputs of the same age (97th percentile)
  • Good Attention Score compared to outputs of the same age and source (77th percentile)

Mentioned by

news
1 news outlet
blogs
2 blogs
policy
12 policy sources
twitter
5 X users
patent
7 patents
wikipedia
1 Wikipedia page
reddit
1 Redditor
podcasts
1 podcast

Readers on

mendeley
998 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Article details
Title
Fair Prediction with Disparate Impact: A Study of Bias in Recidivism Prediction Instruments
Published in
Big Data, June 2017
DOI 10.1089/big.2016.0047
Pubmed ID
Authors
Abstract

Recidivism prediction instruments (RPIs) provide decision-makers with an assessment of the likelihood that a criminal defendant will reoffend at a future point in time. Although such instruments are gaining increasing popularity across the country, their use is attracting tremendous controversy. Much of the controversy concerns potential discriminatory bias in the risk assessments that are produced. This article discusses several fairness criteria that have recently been applied to assess the fairness of RPIs. We demonstrate that the criteria cannot all be simultaneously satisfied when recidivism prevalence differs across groups. We then show how disparate impact can arise when an RPI fails to satisfy the criterion of error rate balance.

Login to access the Attention Digest and the Sentiment Analysis related to this output.

Timeline Attention over time Attention Score history
Login to access the full charts related to this output.
Activity
Login to access the full charts related to this output.
X Demographics

X Demographics

The data shown below were collected from the profiles of 5 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley demographics

Mendeley demographics

The data shown below were compiled from readership statistics for 998 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Login to view Mendeley reader trends over time.

Geographical breakdown

Geographical breakdown
Country Count As %
United States 2 <1%
Unknown 996 100%

Demographic breakdown

Readers by professional status
Readers by professional status Count As %
Student > Ph. D. Student 164 16%
Student > Master 114 11%
Researcher 95 10%
Student > Bachelor 58 6%
Student > Doctoral Student 42 4%
Other 134 13%
Unknown 391 39%
Readers by discipline
Readers by discipline Count As %
Computer Science 234 23%
Social Sciences 84 8%
Engineering 39 4%
Unspecified 30 3%
Business, Management and Accounting 27 3%
Other 157 16%
Unknown 427 43%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 76. 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 01 August 2026.
All research outputs
#702,287
of 34,271,801 outputs
Outputs from Big Data
#14
of 304 outputs
Outputs of similar age
#10,909
of 365,112 outputs
Outputs of similar age from Big Data
#2
of 9 outputs
Altmetric has tracked 34,271,801 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 304 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 16.2. This one has done particularly well, scoring higher than 95% 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 365,112 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 97% of its contemporaries.
We're also able to compare this research output to 9 others from the same source and published within six weeks on either side of this one. This one has scored higher than 7 of them.