Should we collect race-based data?

It’s an evermore bewildering world. Especially for those of us who’ve been around the news story block countless times, only to end up where we started.

Except the enlightenment of the thing — a purported gaining of wisdom — has been turned upside down.

Thirty-one years ago, a young Toronto staff inspector by the name of Julian Fantino triggered a furious public debate when, speaking to North York’s committee on community, race and ethnic relations, he revealed a clutch of race-based crime statistics. His figures indicated that, while Black residents made up just 6 per cent of that community, they accounted for 82 per cent of robberies and muggings, 55 per cent of purse-snatchings and 51 per cent of drug offences in the previous years.

The data was a terrible — and crude — indictment of an entire sector of society, absent any societal or cultural context, without acknowledgment that certain communities have been historically over-policed, disproportionately arrested and excessively convicted or pleaded out by lazy lawyers.

But the numbers gave oxygen to racist beliefs and legitimized harmful stereotypes.

The ensuing controversy led ultimately — credit particularly such organizations as the Urban Alliance on Race Relations and the Black Action Defence Committee — to the banning of race-based data by police across the province.

Now, more than three decades later, we’re told that there’s been a rethink and police should document that information, with a view to eventually expanding the data grab to a broad range of scenarios, including stops, searches, arrests and interactions with police involving use of force.

For the greater public good. For, inside out, a better understanding of how law enforcement deals with visible minorities, with the obvious presumption of: unfairly.

The Ontario Human Rights Commission has labelled the proposal a “historic step.” History does keep repeating. What was righteously wrong 30 years ago is virtuously correct today — because the telescope is pointed the other way. Not on crimes committed by a definable group — which was a sloppy parameter — but to help identify mistreatment of that group in their engagements with police.

But oh yes, they’ll do it better this time, analytically, with more intelligent interpretation of the data collated.

Presently, police can gather that data only in the context of extremely limited carding — profiling — as overhauled by Queen’s Park in 2017.

“The policy mandates an analysis that takes into consideration more than just the statistics or the data about race,” Toronto police board executive director Ryan Teschner told the Star Tuesday. “It requires not only the Toronto Police Service, but also an independent expert to take a look at the race-based data and a whole host of contextual factors that an independent expert deems relevant in order to pull all of this together. Then putting the information out so that it can drive informed public discourse and informed policy decision-making.”

So, a first step and they’ll figure out next steps over months, possibly years, to come. Board member Uppala Chandrasekera explained further: “We’re specifically looking at interactions.” Specifically, to begin next year, requiring recording race in existing use-of-force forms. “Our focus is on interaction and engagement with very specific communities.”

But, while data may be subjective-neutral, how it can potentially be exploited is not, even for the reverse purpose of the noble intent.

“If someone wants to take that data and misinterpret it in terms of engaging in a racist discourse,” Chandrasekera added, “that is not our intention in any kind of way. Making the data very transparent and available to a multitude of different types of analysis is what’s going to offset that possibility. I can take anything and twist it for my purpose, especially if it’s an ill-intentioned purpose.

“This is a process where we’re giving communities, particularly racialized communities who’ve been asking for this, a tool which will enable them to actually quantify their stories. It’s about truth-telling from a quantitive perspective.”

Notisha Massaquoi, co-chair with Chandrasekera of the board’s anti-racism advisory panel, insists BADC and the Urban Alliance have been extensively consulted and are supportive.

“We’re very mindful of the history that they’ve had with this whole conversation. Times have changed and we understand now how strong and powerful this policy will be in terms of supporting Black communities.”

Mayor John Tory is also a police board member and approves of the proposal, though he hasn’t seen all the details yet. “What you had (pre-ban) was the use of this data in problems that exist with bias in policing by one party that’s involved in it,” he told the Star, “as opposed to what you’re going to see now, which is the collection of the information, the analysis of that information by an independent civilian person or body, and then the identification coming out of that analysis of problems that exist. Or don’t.”

In fact, there have been a series of reports since the ’90s which have advocated for the collection of race-based data, if interpreted properly by an objective third party.

“I just think today, people just get the fact that if we don’t collect the data, then you can’t analyze it,” Tory said. “If you can’t analyze it, then you won’t identify problems and it allows people to claim that those problems don’t exist because there’s no evidence. And that leads to ultimately the denial or perceived non-existence of problems which we know exist.

“The mandate the police board will have to actually act on that analysis and do something about it … will be a huge step forward in the restoration of trust in policing and the elimination of any bias that may exist in policing. That’s a huge step forward.”

Three decades ago, when Toronto was experiencing a spike in violent crimes — much like today — all sorts of fundamentally racist tropes were put forward to allegedly explain why Black people were disproportionately involved in crimes. We’re more aware now of what lies beneath — barriers in education, public housing in unsafe neighbourhoods where crime festers, a lack of opportunity. The term “Black” was applied generically when it was quite obvious that a subgroup was most extensively involved — a group of young Jamaican men, many of whom had been reunited with their mothers in Toronto years after being left behind in Jamaica to be raised by extended family. In 2010, there was a crackdown by both Toronto and Jamaican police, specifically on the Shower Posse gang, which was involved in drug and arms trafficking in both countries and took two years to dismantle.

In days of yore, there were only a handful of street gangs active in this city, coalescing along cultural lines. Today, there are umpteen gangs, but they still align ethnically — Somalis and Tamils and Vietnamese and more. These are often disaffected young men, part of more recent refugee influxes, who haven’t been able to find a place for the same reasons of alienation and wanting.

Would it not make sense, if we’re going the data route, to track ethnicity rather than race as a more significant factor?

“It’s not about understanding a community,” Massaquoi countered. “This data is about understanding how police interact with that community.”

Chandrasekera: “We know there’s a huge overlap between criminogenic factors and social health. Barriers to access in education, housing, employment, all of these have an impact on our health and our involvement or not with the criminal justice system. What will be important to see is if there is a disproportionate impact. If the data shows there is increased (police) engagement with a certain community, this is where we’ll rely on our academic experts and others to say, ‘You know what? This is shining a light on a particular issue.’ That’s where the contextual factors will be very, very important to embed in the analysis.”

Massaquoi: “If the data was going to show us an overrepresentation of criminal activity in particular communities, I wouldn’t have had to fight so hard for this policy to be passed. I don’t think that’s what we’re going to find.”

You say data, I say data processing.

Sometimes, as we’ve seen in the past, you uncover what you were predisposed to find.