Tag Archives: Data

Racial Injustice In NYC Revealed By Data

Every morning, New York City police officers receive insights from computers, directing them to areas where crime is likely to occur. If that sounds similar to the 2002 sci-fi movie Minority Report, it’s because it is quite similar. While the NYPD may not use futuristic “precogs” to target specific individuals before they commit a crime, the department does use a computer program to identify “hotspots” where crime is likely to occur. But in both the movie and in New York, the prediction is just that — a prediction. The actions taken by the police are the reality, and unfortunately, sometimes a racially unjust reality.

We analyzed the NYPD’s stop-and-frisk program and found that while the overall number of incidents has declined significantly since a redesign of the policy, there is an unsettling increase in racial imbalance in at least eight NYC precincts.Click to tweet

Stop and Frisk

The “stop-and-frisk” program

From its inception, the stop-and-frisk program sought to reduce crime by giving policing officers the authority to identify and search suspicious individuals for weapons and contraband. The stop-and-frisk tactics gained greater traction with the implementation of CompStat in the 1990s. This process employed Geographic Information Systems in order to map crime and identify problems, taking into account past criminal trends in neighborhoods and allowing officers to quickly address crime spikes. Over the past two decades, the practice evolved into the “stop-and-frisk” program we know today – where at one point stops exceeded over fifty thousand a month, largely targeting minorities. In 2012, racial minorities accounted for 92% of all stop-and-frisk incidents. The disparate impact of the stops on minorities led to public outcry and received extensive coverage from The New York Times, Slate, The Atlantic, eventually resulting in a legal case against the city.

Today, the NYPD is hoping to improve its’ policing tactics and is conducting a two-year trial run with predictive policing software, Hunchlab. While the Hunchlab algorithm does not take into account individual characteristics, such as race, ethnicity, or gender, it does incorporate factors such as “socioeconomic indicators; historic crime levels; and near-repeat patterns to help police departments understand and respond more effectively to crime.” Certainly, this brings doubt as to whether or not the software will reduce the disproportionate burden of current policing tactics on minority communities. In the meantime, we should be questioning what individual precincts can learn from others and what they can do to improve their policing practices for all.

Breaking it down by precinct

While prior analyses of the stop-and-frisk program mainly focused on the overall discriminatory practices of the policy, we looked at precinct level data to identify areas with the largest disparities between the racial makeup of the community and the racial makeup of the stop-and-frisk incidents within that community. In other words, holding all else equal, in a precinct where 10% of the community is black, black residents should represent only 10% of the total number of stop-and-frisk incidents occurring in that community. If that number is substantially higher than 10%, then racial profiling may be contributor to this disparity. To compare precincts we created an index called the Racial Disparity Index (RDI) to compare across precincts. 

Our study showed that neighborhoods with the greatest racial disparity between the race of people stopped and the racial makeup of the residents are predominantly white neighborhoods, with the exception of Chinatown. These neighborhoods include the Upper East Side, Greenwich Village, Upper West Side, Park Slope, Tribeca, Soho, Brooklyn Heights, Midtown East, and Chinatown. For example, Precinct 19, part of the Upper East Side, recorded the highest level of racial disparity in 2015, reaching a score of 31.41 on our index. Out of the 278 stops conducted there, nearly half were against black residents, even though they make up a mere 2.3% of the population.

all-precincts-rdi

Many precincts showed an increase in Racial Disparity Index (RDI) scores over the past five years Click to tweet , even after the NYPD enacted reforms in attempt to reduce racial profiling within the program. While in 2012 the Racial Disparity Index (RDI) was at 7.6 (average over all precincts) it went up after the program changed to a 8.9 in 2015 conveying that the stop-and-frisk program has become more imbalanced. Or said differently, if you are not part of the racial majority of a specific community, you still have a higher likelihood of being stopped. In 2015, out of the 270 stops that occurred in Precinct 84, Brooklyn Heights, three-fourths of them were carried out against black residents, a minority group in the community.

It is unclear what leads to this increased disparity over time, but we see several plausible reasons. For one it could be the way crime is conducted has changed between 2012 and today. Criminals are mobile and might move into other precincts changing the RDI. In this case we are seeing a reflection of this occurrence. On the other hand it could be that the system as such is skewed. Here are two possible elements: the human and the machine.

Something is working

Undoubtedly, racial discrimination has plagued the stop-and-frisk program from the beginning. As one can see in the image below, public outcry and the resulting policy reform led to a dramatic decrease in the number of stops conducted.

Number of Stops

At the same time, the overall effectiveness of the policy, as measured by the percentage of stops in which contraband is recovered, has increased. From this, we can infer that the stops have become more effective.

Our analysis breaks down the success of the program by precinct. For example, Precinct 72, which includes Greenwood and Sunset Park, has managed to lower the racial disparity of stop-and-frisk occurrences while simultaneously increasing the effectiveness of its’ tactics from 3.69% to 7.38% over the past five years. The racial makeup of the precinct is well mixed between Asian, Hispanic, and White. Meanwhile, Precinct 78 (Park Slope), which is adjacent to 72, has seen the greatest increase in disparity, from an RDI score of 18.17 in 2010 to 25.13 in 2015, while only marginally improving its’ contraband discovery. This suggests that the increased policing against racial minorities has not proven to be effective in recovering contraband in the precinct.

Percentage of Contraband

Other Possibilities

Racial disparity is not the only issue that predictive policing algorithms are facing. It is important to keep in mind we use the metric “percent of contraband found” as a key performance indicator for the program, as the city uses this criteria. This might be a misleading metric. For example, let’s assume the amount of contraband in the city is increasing as the city becomes less safe. The likelihood of finding contraband during a stop-and-frisk incident would then increase as well. In this scenario, that doesn’t translate to a successful program, but rather displays an indication of the opposite. Moreover, these algorithms only spot correlation based on past data. Sending police to a hotspot does not expose the underlying cause of why the crime actually occurred.

While predictive policing is a booming sector with software coming from companies such as Azavea, PredPol, and Hitachi, an analysis of the data from the NYPD’s stop-and-frisk program reveals legitimate concerns to the data-driven approach. How city tactics change over time will need to be closely monitored to ensure a fair and non-discriminatory approach, while also taking a look at the root cause of criminal activity.

Who we are

The analysis of the NYPD’s stop-and-frisk program was performed by Maciej Szelazek, Maggie Barnes and Derek Cutting together with Lutz Finger as part of his course on Data Products at Cornell Tech. Further insights into data can be seen here.

This article was first published at Forbes.

data to manager

How Cornell Trains Future Data Managers

How do you create amazing new data driven products! By studying deep, deeper and the deepest machine learning algorithms? Nope – by enabling business folks to talk and think data .

Recently I had the opportunity to test this theory. I taught a course for MBA students at Cornell University’s Johnson Business School and Cornell Tech campus. In the course, we covered the basic components of data science including modeling, visualization scraping technologies and databases. At the end each team built a stunning data app: from predicting the of future Starbucks locations to setting prices for old vinyl records.

data to manager

It is the business mindset that drives many of our product ideas. Thus while data scientists are tough to hire (I am hiring at LinkedIn – join our great team) it is the business focus that is missing. McKinsey recently called out that we face a shortage of about 150k Data Scientist as well as 1.5 million managers . Really? Are those data scientists so hard to manage that we need 10 managers to take care of them? No. The truth is that we are missing data minded managers as we need to include a business-related component in any data discussion. If you hear “actionable analytics,” it only means something like “business thinking inside”.  Analytics should be focused on an action to improve or change our business.

My course at Cornell had two main objectives: to take away the fear of big data and to create a common language for MBAs to use. But how do we “take away the fear”? The answer is by building your own data applications. Many data science syllabi often teach students tools such as R (a programming language for statisticians), Python, and the like. Don’t get me wrong: these are great tools, but they are useless for teaching MBAs. No one will remember a few months down the road how to even load a dataset.

Thus I focus on simplicity. For data scraping we used import.io (a great tool founded by Andrew Fogg), for visualization we used plot.ly (a very simple visualization tool by Matt Sundquist) and for the predictive layer we used eitherBigML or Excel. Yes, Excel. It can – with a bit of hand-holding – recreate many Data Science models. It is learning by doing. If you want to dig into this, I highly recommend the book Data Smart from John W. Foreman.

data_product_thinking

Scraping, plotting, and crunching are important… but it does not take long for any smart MBA to ask the “so what question.” So we start with a framework of actionability and applicability discussed in my recent book Ask Measure Learn(find it here). Students learn that data and algorithms are nothing if you cannot create an action or build a product.

The course finishes with a term project where teams can use any data they can get as well as any complexity of model, as long as they define their own product use-case for the app. This complete freedom created amazing results. One person who is an excited collector of vinyl records built an engine to better determine their price point. Another team built an app to find for the best location for party-and-bar-loving MBAs  at a given price. Yet another team analyzed the feedback from various classes to determine how professors need to change to improve their teaching.

More important, some of these projects went far beyond being cool and made a very strong business case. Here are just two examples from our course that showcase what MBAs can do with just a little data science background:

  • Jacob Jordan (Jacob Jordan) predicted with an 80% likelihood where Starbucks will open their next store. With his model, he went even further and analyzed the claim that Starbucks drives gentrification, but could not find a high correlation with typical gentrification factors.

At the end of the course there were many high-quality business apps powered by data. A complete list of all project videos can be found here. This Cornell course is proof for me that in order to unleash amazing capabilities for innovation, companies need to teach business managers basic data science techniques .

This fall I will teach this course again at Harvard Business School (together withProf. Datar) as well as at Johnson’s Cornell. Let’s see what kind of innovation we will get!

lutz-cornell

(Lutz Finger – talks about his book “Ask Measure Learn” at Cornell University (photo: Bryan Russett))

This article was first published in Forbes.