Can Data Be Racist? UK Researcher says ‘Yes’.

David Blatt, Data Specialist for One Vision One Voice, sits down with UK academic David Gillborn to discuss the myth of neutral, objective data collection, how racism can be embedded in the way an organization collects data, and how to overcome this challenge.

David Gillborn, Professor of Critical Race Studies and Director of the Centre for Research in Race & Education (CRRE) at the University of Birmingham, UK

David Gillborn is Professor of Critical Race Studies and Director of the Centre for Research in Race & Education (CRRE) at the University of Birmingham, UK. He is best known for his research on racism in educational policy and practice and, in particular, for championing the growth of Critical Race Theory internationally.

OVOV reached out to Gillborn after reading his paper connecting quantitative data and critical race theory.

Q: You are known for your work on Critical Race Theory (CRT). What is CRT?

A: Critical Race Theory (CRT) views racism as being much more subtle and widespread than is usually recognized. In particular, CRT sees racism (acts that systematically disadvantage one or more minoritized groups) as woven through the everyday routine fabric of how institutions operate – including institutions that believe they are providing a service to all (schools, universities, hospitals, social work etc.).

Q: In your recent journal paper called “QuantCrit: education, policy, ‘Big Data’ and principles for a critical race theory of statistics” you challenge the neutrality of data collection. How would you explain “QuantCrit” to those who do not know a lot about quantitative data or CRT?

A: My co-authors and I tried to identify some principles, inspired by CRT, that could act as a prompt to help people think more critically about race/racism when they are confronted by numbers. This is important because stats are one of the first weapons people use to try and shut down critical debate.

Q: My understanding is that your article identified 5 principles that can impact the neutrality of data collection .Can you state what they are?

A: (1) the centrality of racism, (2) numbers are not neutral, (3) categories are neither ‘natural’ nor given (4), data cannot ‘speak for itself’ and (5) using numbers for social justice

Q: Why do people believe that quantitative data is colour-blind, neutral, factual, and objective?

A: They’re told to believe that quantitative data is colour-blind, neutral, factual, and objective. Most people feel ill equipped to critique numbers. We’re intimidated because we’re told that numbers are complicated and that you have to be clever to understand them. Numbers are constructed by researchers in exactly the same way as interview data. Someone has to choose to collect the numbers – which numbers to collect? When to collect them? Where to look? Who to ask?  Every one of these questions will change the number that you end up with. So don’t ever believe that numbers are just cold facts lying around waiting to be counted.

We’re good at judging qualitative data because we do it all the time in real life; we’re constantly making judgements about what people tell us – we need to develop the same kind of questioning skills around numbers.

Q How is using “race” as a category problematic?

A: The labels that we use to define ‘races’ are socially constructed. So, when someone states that ’race’ is related to something (like unemployment, qualifications, likelihood of being in prison) I suggest that you replace ‘race’ with ‘racism’ – because ‘race’ is just a label. Racism is the process by which the label is related to different life chances.

For example, a news reporter might report some stats and say that ‘Race is a factor in unemployment.’ A lot of White people will assume that means that there’s a problem with minorities that makes them less employable. I think a more meaningful description is ‘Racism is a factor in unemployment’ – that forces you to question where the problem lies – not with the unemployed, but with the system.

Q: If quantitative data suffers from bias and non-neutrality how can it ever be trusted?

A: In the same way that any other data can be ‘trusted.’ We need to make a judgement based on sensible questions about where the data is from. How convincing is it? Whose interests does it serve? Is the finding credible?  No research is automatically trustworthy.

Q: In your paper you state that “one of the tasks of QuantCrit is to challenge the past and current ways in which quantitative research has served White Supremacy.” What do you mean when you say White Supremacy?

A: In CRT we use the phrase ‘white supremacy’ in a much broader way than is traditional. Traditionally people think of ‘white supremacy’ in terms of obvious and crude racism such as fascists and Neo-Nazis.

But in critical race theory we view white supremacy as much more subtle and extensive. A quote that I find useful is from Frances Lee Ansley: “[By] ‘white supremacy’ I do not mean to allude only to the self-conscious racism of white supremacist hate groups. I refer instead to a political, economic, and cultural system in which whites overwhelmingly control power and material resources, conscious and unconscious ideas of white superiority and entitlement are widespread, and relations of white dominance and non-white subordination are daily re-enacted across a broad array of institutions and social settings.”

Q: Many quantitative child welfare researchers have stated that disproportionality in child welfare for Black children is attributable to higher risk, such as higher poverty rates, rather than anti-Black racism in referrals and in the child welfare system. However, critical race scholars, advocates, and members of the Black community believe that anti-Black racism in referrals and in the child welfare system leads to disproportionality. How can quantitative researchers and child welfare database designers apply QuantCrit to resolve this debate?

A: It’s true that some minoritized groups ARE more likely to live in poverty. But that’s not an accident. That’s BECAUSE of anti-Black racism. Similarly, Black folk tend to have lower educational qualifications, their income is more precarious, they’re more likely to get into trouble with the law. ALL of these things reflect the influence of racism. So it’s not an either/or question. It’s not poverty OR racism.  It’s poverty AND racism for many!

QuantCrit can’t resolve this question in a way that everyone will agree. No approach can!

Some statisticians argue that they can compare like-with-like by using regression models to look for the separate influence of different factors. But where racism exists it often runs through the very factors that people try to ‘control’ out. For example, if you ‘control’ separately for things like income, home ownership, educational attainment, parental education etc. – all those things will have been shaped (at least in part) by racism.

Q: How can Children’s Aid Society CEOs, upper management, quality assurance staff, and child protection workers apply QuantCrit to their work to inform their decision making?

A: Don’t accept numbers at face value.

Critically engage with numbers to explore how racism might be at work (often in hidden ways) and think about these issues when collecting data. Think about which groups are questioned and what data is collected and who does the analysis?

The obvious and traditional ways of doing things have tended to produce racist outcomes; so don’t assume that the traditional way is best – why not assume that the traditional way is probably biased. How can you improve on it?

Q: Is there anything else you would like to share to a broad child welfare audience made up of researchers, database designers, CEOs, upper management, child protection workers, etc.

A: Questions about racism feel unbelievably threatening to most white people. Even well-intentioned ones feel unsure about what words to use, how to phrase something… But there is no rule-book here and everyone makes mistakes. The more we work across the usual class/race/gender/dis-ability lines, the more we get used to asking strange questions and thinking in new ways. The easiest thing is to blame poverty – white folks understand poverty and they don’t feel culpable for it. But racism challenges white people on every level and, deep-down, white folk know they benefit from racism, even if they’re certain that they personally are never racist. That’s a hard thing to come to terms with but its essential if you want to try to be part of the solution rather than yet another part of the problem.

Gillborn gave OVOV permission to post his paper on myOACAS. To view and download Gillborn’s paper go to: bit.ly/quantcrit or find it on the OVOV Members Site

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