Rape in India - A study by Juxt in public interest
Police Killings in America
1. Hands up, don't shoot!
A Comprehensive Look at Police Shootings in America
By: Maxwell V. Pederson
2. Introduction and Background
• The fatal police shooting of Michael Brown back in 2014 brought about widespread
outrage and debate across the nation
• The Guardian, a British Newspaper, sought about the reporting of police killings, in
which they found there is no comprehensive database!
• Only as of recently from all the protests and outrage has the FBI decided to create a
voluntary program in which police can choose or choose not to report their justifiable
killings.
• From 2005 - 2012 only 1,110 of the 18,000 police departments reported these justifiable
killings
• Data for this project was gathered from the Guardian's Open Source Police Homicide
database and from FiveThirtyEight’s version of the Guardian data
3. Problem Statement
There has been over 2,100 reported fatalities caused by police since January of
2015. With all the the riots, protests, and uproar caused by certain police shootings
such as Michael Brown, what is the general sentiment about police shootings in
America? Is there reason to believe that police have an inherent bias towards who
they kill? Does the data support the public's sentiment towards police shootings?
Overall Goal: Understand the characteristics of these police shootings to come to a
conclusion on whether the general population’s opinion on police fatalities is
justified by biases police may have towards who they kill.
4. Methods
• Took the FiveThirtyEight data: Started out with 34 attributes and 467 instances
• Only a few attributes consist of the original attributes from the Guardian database (categorical)
• Most attributes are numerical census data added by FiveThirtyEight
• Very unique instances, show the data is fairly linearly-inseparable and may suffer
from the curse of dimensionality
• If columns were used and had bad data/NAs the whole row would be removed
• Most if not all imputation methods will not work well when the data is this linearly-inseparable
with many dimensions
• Extensive use of C5.0 , SVMs , RFs , and BBNs are used in this presentation.
13. Bayesian Belief Network
• Probability that a person is armed
given they are in one of the poorest
districts, given a certain race: 65 ~
69%
• Probability a person was killed by a
gunshot given they were armed:
90% (also the same if not armed)
• Probability a person is armed given
they are a male: 74% and 64% given
they are a female
14. Predictive Modeling Performances
• All of the algorithms have low
accuracy and low sensitivity
• The low sensitivity shows that
the algorithms are
misclassifying unarmed
people as being armed
• Just as in the real world, each
event classification is
extremely different hence the
poor accuracies
15. Conclusion
• The general population of twitter shows a negative sentiment towards police shootings, but it must be
kept in mind the population that is on twitter, young adults. This could serve as an “echo-chamber” effect
as seen in the election.
• Predictive models don't do well on this data because it's too specific and makes the data linearly
inseparable, yet in real life we aren't getting enough of the picture to classify if a person is armed or not.
• The models mostly misclassified people who aren't being armed as being armed, which seems to reflect
controversial killings today.
• Police may or may not have biases, but it can be seen that if these advanced algorithms can’t classify
properly, imagine being the person in the situation when a police call comes in.
• The next steps would be to get a fuller picture on police killings and in general more data. Coupled with
top notch non-biased data scientists, maybe predictive forecasting of crimes could be done.
• As seen a Wall Street Journal however, algorithms aren't biased, the people who work with them are. For
me, I definitely tried to get certain results out of the models, which shows my bias.
16. References
Chen, Eugene. "Map on MapInSeconds.com." MapInSeconds.com by Darkhorse Analytics. Darkhorse
Analytics, 2016. Web. 15 Dec. 2016. <http://mapinseconds.com/>.
McGinty, Jo Craven. "Algorithms Aren't Biased, But the People Who Write Them May Be." The Wall Street
Journal. Dow Jones & Company, 14 Oct. 2016. Web. 15 Dec. 2016. <http://www.wsj.com/articles/algorithms-
arent-biased-but-the-people-who-write-them-may-be-1476466555>.
Swaine, Jon. "About The Counted: Why and How the Guardian Is Counting US Police Killings." The
Guardian. Guardian News and Media, 2015. Web. 15 Dec. 2016. <https://www.theguardian.com/us-news/ng-
interactive/2015/jun/01/about-the-counted>.
Flowers, Andrew. "Fivethirtyeight/data." GitHub. FiveThirtyEight, June 2015. Web. 15 Dec. 2016.
<https://github.com/fivethirtyeight/data/tree/master/police-killings>.
Editor's Notes
Just found 12/15/16: The justice department says it's now trying to use information from the police and open sources such as The Guardian to collectively fill in the gaps!
Link: http://fivethirtyeight.com/features/the-government-finally-has-a-realistic-estimate-of-killings-by-police/
The plot on the left is that of Eric Garner, who was choked out by police and had no weapon on him. It is clear the sentiment is very angry from the wordcloud. The plot on the right is that of Sylville Smith, who did have a weapon on him but dropped it and the policeman fired and killed him. This policeman is currently on trial to see if he will be indicted for the killing. It is clear the sentiment is very negative towards the fact Sylville Smith was killed. What is interesting is that much of the negative sentiment is over the fact that Black Lives Matter groups are fighting Blue Lives Matter groups on twitter.
There is a decent correlation between share_black and median household income. Its apparent though for both however that the dispersion of whether a person is armed or not is fairly random.
These bar graphs show that in general , most people were armed and most people were killed by gunshots. White people make up the majority of people killed, followed by black people.
Although there seems to be a strong correlation on the last slide between poorer counties being predominantly black and richer counties being white according to those who were killed in those counties, this slide is conflicting in those who were armed versus those who were not. Coming up it will be seen that this randomness will hurt the predictive algorithms.
While a randomforest model shows that many census data attributes such as p_income or county_income is the most important, they also are very strongly correlated to each other (after all, some of these features such as comp_income was calculated from dividing h_income by county_income). This is a contradiction as the RF model shows they should remain but the correlation matrix shows some of these features need to be deleted. I chose to delete a few of them so there wouldn't be a correlation, yet the most important ones would still remain as shown in the RF model. Notice raceethnicity is not used often , compare to next slide.
Done over 10 iterations: These are the attributes that are used the most:
Attribute usage:
100.00% age
100.00% raceethnicity
100.00% cause
100.00% county_bucket
100.00% pov
100.00% urate
100.00% college
70.97% share_black
54.52% share_white
33.23% pop
23.87% share_hispanic
A tuned SVM model doesn't do much justice. It somehow made most of the instances support vectors (which defines the classification lines) which can be seen as X's on the left hand graph and boxes around circles on the right hand graph (219 support vectors here). Clearly it's ineffective here. Its supposed to work well on non-linearly separable data and data that has the problem of the curse of dimensionality but it doesn't here. Knncat which similarly should be good on this type of data also didn't work well.
The query model doesn't work very well here. The reason all the percentages are pretty much the same regardless of what is given is because the model doesn't do much better than the data estimates that are already known. For instance being armed and being killed by a gunshot primarily are the primary frequencies and hence have the largest conditional probabilities here.
Wallstreet Journal Article: http://www.wsj.com/articles/algorithms-arent-biased-but-the-people-who-write-them-may-be-1476466555
Its interesting how they tried doing predictive forecasting of crimes and contacted certain people that may commit future crimes even though some of them had never had a criminal history before! It was because of several data points that listed a certain region of the South Side of Chicago of having certain people who may commit crimes in the future.
