Welcome to #First100Days!
The #First100Days series will “bear witness” to the next 13 weeks of the Trump administration and the climate in America and then respond openly in writing, dialogue, and debate in the hopes of fostering better communication among writers and partisans alike (although the essays and pieces do not have to be political in nature). We’re looking to help give voice to honest and thematic essays from all layers of the political spectrum and across all GMP sections.
All opinions are those of the author and not necessarily of Good Men Media, The Good Men Project, or our editors.
Years ago, I was waiting to be seated at an Italian restaurant when I noticed a framed certificate on the wall.
It proclaimed this business was the best Italian restaurant in the area, according to an annual survey by a local newspaper. There were several Italian restaurants in town, so I thought this was notable.
I assumed that the survey would also offer certificates for other common cuisines in the area: Chinese, Mexican, seafood, and so on. So, I was surprised when I came across the survey itself. The dining categories were “Best Restaurant,” “Best Ethnic Restaurant,” and “Best Italian Restaurant.”
As someone of Italian ancestry, I had to wonder why our cuisine was singled out. Was it not normal enough to fit under “Best Restaurant”? Or not unique enough to be “Ethnic”?
Whether intentional or not, this categorization gave Italian restaurants particular attention. Rather than competing with all the restaurants in the area for a single award, they only had to compete with other Italian restaurants for a more specific award. Other restaurants were flattened into broader categories. Their differences were erased. Italian food stood out simply by the design of the survey categories.
The design of survey categories is closely studied in the fields of psychology and social science, and by pollsters. Among others, the Pew Research Center is well-known for its political and social surveys, and has put tremendous thought into how it designs its questionnaires.
For example, in 2002, they asked half of a group whether it was more important “for President Bush to focus on domestic policy or foreign policy.”
Of these two categories, 52% chose domestic, and 34% said foreign. The rest were undecided.
However, Pew changed a few words for the other respondents, asking whether it was more important “for President Bush to focus on domestic policy or the war on terrorism.” With this small change in words—with a direct focus specifically on “the war on terrorism”—the responses flipped: 33% chose domestic, and 52% chose the war on terrorism.
Because it was more specific, that category drew people’s attention.
Fortunately, local restaurant awards and political surveys are rather benign. Less fortunately, history is rife with more dangerous examples.
One might be the Uniform Crime Report (UCR). Since 1930, the FBI has compiled crime data provided voluntarily by law enforcement departments around the country. The FBI then presents these statistics through UCR, from various perspectives: arrests, victims, weapons used in homicides, and more.
Prior to the UCR, crime data was collected locally, particularly in major cities, and published in newspapers. For example, the Chicago Almanac listed 1920 arrest statistics for white ethnicities such as American (I assume at least first or second generational), Italian, Irish, German, and others, as well as Colored, a label which seems to include Asians in other statistics.
However, with the first UCR, all the European races were merged into a single description: “white.” There was initially some distinction between “native-born whites” and “foreign-born whites,” but that disappeared by the 1940s.
Non-whites were classified as Negro, Other, or Unknown. Some data tables also included Indian, Chinese, Japanese, and Mexican.
This continues today. Nowadays, the UCR recognizes race as “White,” “Black or African-American,” “Other,” and “Unknown”—and “White” includes anybody from European or Middle Eastern descent. Then, it lists ethnicity as “Hispanic or Latino,” “Not Hispanic or Latino,” and “Unknown.” In some data tables, ethnicity and race are merged. Arrest data includes additional categories for “American Indian or Alaskan Native” and “Asian or Pacific Islander” while eliminating Hispanic and Unknown (the FBI’s other crime database, NIBRS, uses essentially the same categories).
Even though white ethnic groups are by far the largest group in terms of numbers, they receive less focus in the UCR.
By collapsing all the European races into a single group, the UCR “erased the category of the white ethnic criminal,” as Dr. Khalil Gibran Muhammad noted to Bill Moyers. With race categorizations of White, Black, Other, and Unknown, “Black became the single defining measure of deviance from a white norm.”
Specific things stand out more than general things.
The UCR also includes gender, age, and generic municipality size as primary identifiers. However, the FBI readily points out that the UCR does not incorporate environmental data about wealth or poverty; social or religious issues; education; mental health; and many other data points relevant to understanding crime. Even 500 years ago, Thomas More famously noted there was more to crime: “If you suffer your people to be ill-educated, and their manners to be corrupted from their infancy, and then punish them for those crimes to which their first education disposed them, what else is to be concluded from this, but that you first make thieves and then punish them.”
The design of the UCR categories reinforces the ideas that crime can be studied without environment, and that race can be a primary measure of criminal activity.
As lawmakers and law enforcement act on this, they may create anti-crime models that focus on the wrong things. For example, New York City’s famous “stop and frisk” policy targeted blacks and Latinos more than whites, even though the data shows whites were a third more likely to be carrying contraband and twice as likely to be carrying a gun. Direct racial profiling was caught on tape, but it’s a localized example of the systemic profiling represented in the stop and frisk policy, and more broadly in the UCR categories.
Data scientist Cathy O’Neil notes that “models are constructed not just from data but from the choices we make about which data to pay attention to — and which to leave out. Those choices are not just about logistics, profits, and efficiency. They are fundamentally moral” (emphasis added).
We’re seeing this kind of moral decision play out in the early days of the Trump administration.
For example, the new President has signed an order that requires Homeland Security to publicly release a weekly list of crimes committed by immigrants.
The immigrant crime rate is consistently lower than the national average. So, what purpose does this weekly list serve except to reinforce pre-existing racist beliefs that immigrants are criminals?
President Trump is not one to stick to data, however. For example, he recently said that “the murder rate in the United States is the highest it’s been in 45 years,” when in fact the murder rate is at a 45-year low, at least according to UCR data. Even violent crime is at its lowest in 30+ years.
President Trump posted a statement decrying the “dangerous anti-police atmosphere in America” and vowed to “end it” through “more law enforcement, more community engagement, and more effective policing.” However, anti-police sentiment is not new, and I wrote previously about this in response to many people (mostly white) who seemed clueless on the topic.
Anti-police attitudes are the result of failed law enforcement systems that benefit some and beat down others. Such attitudes will not dissipate until the inherent racism and economic biases in the system are dealt with. That means examining the categorizations of crime (as in the UCR), and improving the environments that are more likely to produce crime. Does this mean more cops and bigger guns in black and immigrant neighborhoods? That seems to be what President Trump has in mind, because he doesn’t question the categories, and he appears unlikely to connect the racial disparity in education (and other systems) with crime reduction.
Categories represent the biases of their designers. Regardless of who is presenting data and models — whether for restaurants or criminals—it is important to look at the choices being made. What is put in? What is left out? Whether by agenda or by accident, is special focus brought to some groups while obfuscating others?
At that Italian restaurant I visited, the food was pretty good. I can’t say it was the best in the area without trying the others. But in the end, that survey and its unusually-focused categories has a limited effect on my behavior.
On the other hand, at the level of laws and law enforcement, categories have a significant effect on our country. Let’s make sure we are paying attention.
Read Tor de Vries every week here on The Good Men Project!