Reverse engineering secret algorithms: factors thatinfluence parole decisions
Junjie Jiang '19
Mentor: Nathan Ryan, Vanessa Massaro
Bucknell University, Lewisburg, PA.

Abstract
We used web scraping and GET methods to acquire parole data from government websites. We then used Python and R to clean these data. We
made sheets for data from different states or cities, including South Carolina and Virginia. Then, we used R to analyze the data to see the influence ofdifferent attributes on final parole decisions. Preliminary results according to logistic regression showed that race and sex do not appear to significantly
influence final parole decisions. More careful and thorough analyses should be made for more accurate results.

Introduction

Data Collection

As computer programs are playing anincreasing role in the correctional system, it isimportant to make sure that these programs
are actually fair and sensitive attributes ofcriminals will not have any influence on thefinal decisions. The sensitive attributes include
but are not limited to sex, race, age, zip code.
These attributes are not relevant to the crimeand should not be considered as reasons forsentences. Therefore, our research collects
data and metadata of criminals along with
their parole decisions and analyzes whether
their parole decisions are made fairly
according to mathematical and statisticalanalysis.

There are several websites of different statesthat provides the public with parole
information of criminals.
For South Carolina, since the state offers thedata with the website, we used fetch queries
to get the html file with different urls bychanging the id included in the url. Then, wefigured out the html elements that containthe information we need and stored theminto a csv file.
For Virginia, the state provides the data in theform of pdf files online. Therefore, we usedTabula, a PDF file scraping software to acquire
the data of the criminals and manually adjust
some formatting errors.
After collecting the raw data, we used scripts
to reorganize the data so as to facilitate thefuture analysis.

BackgroundIn some states, the parole decisions are madeby computer softwares that are provided byauthorized third-party companies. With some
information of criminals given to the software
as input, the software will provide thedecisions without transparency of how thedecisions are made. Therefore, it is necessary
for people without access to the black box
algorithms to be assured of the equality
within the system. The entire decision making
process needs to ensure that no irrelevant
attributes including but not limited to sex,
race, age are of any influence.

Data Analysis
Information from different states includes
different sets of attributes. All of them include
sex, race and age. Therefore, our data analysis
basically focuses on the relationship between
parole decisions and these attributes.
We used R to conduct our statistical analyses.
First we removed the data points that are
obvious outliers. Then we quantified all linguistic
variables to numerical values. Finally we usedlogistic regression to statistically figure out if
certain attributes are determinant for parole
decisions.

Analysis for Data from Virginia

Results
We did logistic regression analysis for two statesseperately and did age, sex, race and three
attributes together for each state.
• South Carolina• Age ~ Decision: p < 0.001• Sex ~ Decision: p < 0.001• Race ~ Decision: p > 0.1• Age + Sex + Race ~ Decision: Same as above• Virginia• Age ~ Decision: p < 0.05
• Sex ~ Decision: p > 0.1• Race ~ Decision: p < 0.001• Age + Sex + Race ~ Decision:• Age: p > 0.1• Sex: p < 0.1• Race: p < 0.001
Statistically, p < 0.05 represents that a certain
attribute is significantly relevant. Therefore, forSouth Carolina, age and sex are significantly
relevant and for Virginia, race is significantly
relevant. Since the sample size of female isrelatively small (124/1639 for SC and 23/1773 forVA), the influence of sex may be amplified.

Conclusion
According to the preliminary results, it showsthat in South Carolina and Virginia, attributes
such as age, sex and race are correlated to theparole decision making. More specifically,
people with less age, white color and female
sex are more likely to receive positive paroledecision.
Analysis for Data from South Carolina
Sample Raw Data from Virginia

Acknowledgements
Schotz Family Fund for Interdisciplinary Studies