Data LIES—Because the EYES that SEE aren’t Innocent

Our vision, however wide we think it is, is always tinted by our experiences, our fears, and the things we’ve conditioned ourselves to acknowledge as real or as not existing. We might notice a smiling Santa Claus on a billboard yet walk right past a homeless mother nursing her baby in the cold – faceless in our gaze.

In the world of research especially social sciences, I encounter this reality every other day. Through my PhD journey - especially with my background in mathematics and finance -

I’ve come to realize that the core achievement in my pursued title of “Doctor of Philosophy” is the humility to acknowledge my own biases and the courage to unlearn my “truths”, embracing the validity and truthfulness of others' perspectives.

For instance, in my research, which focuses on the intersection of urban and socioeconomic well-being—particularly in housing and social protection—I now acknowledge the honesty of duality of existence in rapidly urbanizing Sub-Saharan Africa. That the person is irreducibly both urban and rural – a moving fluidly that the dominant data parameters are simply not equipped to see with their binary presetting of “either/ or”. It is the beauty that civil rights lawyer Kimberlé Crenshaw taught the world to see about the irreducible multiplicities of our identities —the intersectionality.

The deceit in data manifest in two keyways:

First – in what we choose (or are taught) to see as data. Here, it’s the decision on the indicators and parameters that fit our world view. Perhaps at its most pervasive expression is the complete erasures of identities and knowledge.  

Cathy O'Neil so elegantly elaborates this in her book “Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy”. One chapter focuses specifically on predictive policing algorithms that disproportionately target Black and minority communities. O'Neil argues that these mathematical models, though appearing neutral, are often flawed and biased, reinforcing existing societal biases.

Second – in how we choose to interpret and make sense of the data. It’s one thing to collect it; it’s another to see and acknowledge what is right before our eyes. Here, our familiar comforts and unconscious biases determine what we prioritize, overlook, or dismiss. The field of “undone science,” suggests a couple of reasons why this is the case – here I touch on two:

One - the “bandwagon effect,” that subconsciously makes us wary of straying from the familiar, of seeing the world differently. 

We get caught up in “follow the leader” “copy paste” “boiler plate” “replicate” “path already beaten” “tried and tested” comfort, like a crowd on a busy street moving in sync, even if the signs around us are pointing in a different direction. This is a major critique levelled on the idea of “replicating” western solutions to the Sub-Saharan African context.

Second - something colder: the “chilling effect” which plays out in a world riddled with inequality and injustices. Here, certain voices are silenced not because they lack truth or value, but because power structures dictate they don’t exist or don’t matter. 

There’s a story within the world of science that illuminates this all too painfully. The story of Saartjie Baartman, a Khoikhoi woman from South Africa, who was taken to Europe in 1810. Here she was nicknamed the “Hottentot Venus,” and exhibited as a spectacle because of her unique body features, paraded around like an oddity, stripped of her humanity.

After her death on 29 December 1815 at the age of 26, less than 5 years after arriving in Europe, the famed scientist Georges Cuvier (1769 – 1832) studied her remains. He noted her intelligence—she had, after all, learned French and English in the short time she’d been there. He even recognized her emotional depth - her tears and often physical resistance to further indignities. But despite all this, Cuvier’s final judgment was chilling: he categorized her as “subhuman,” an evolutionary step closer to orangutan than to the European humans in his circle. Classified as “sub-human” her exhibition continued even after her death with her brain, skeleton and sexual organs on display in a Paris Museum until 1974. Saartjie’s humanity is only restored 197 years later in 2002 following the unrelenting campaign of the people of South Africa. Upon the political end of apartheid and election of Nelson Mandela as president in 1994 – South Africa formally requested the repatriation of Saartjie Baartman from France. The legal and political debates embellished with the science biases took 18 years to resolve. Finally, Saartjie was granted the dignity of burial by her Khoi San people a desire echoed on her gravestone set on a hill overlooking Hankey, in the Gamtoos River Valley considered her birthplace – “to restore dignity to her, her ancestors and living descendants”.

Today, Cuvier's legacy is a complex one. On the one hand he is remembered as a pioneer in paleontology, in the scroll of respected member of the French Academy of Sciences and the Royal Society in London, and knighted as a Baron by Napoleon Bonaparte. At the same time, he is the embodiment of the dark side of “scientific racism” with his legacy is haunted by his inability to see beyond the biases of his time. And that’s the risk we all run—the risk of letting our vision be clouded by our own limited visions colored by our realities, fears, and assumptions.

As a PhD candidate for Social Transformation, striving to translate my data into meaningful action for my community, this is my daily struggle. I try to remind myself of this story whenever I sit down to engage data. 

I ask myself, “Who am I not seeing? What voices am I shutting out?” It’s small questions but  powerful ones, reminding me that data, like people, is complex, messy, and full of contradictions. And perhaps that’s where its true beauty lies—in the gaps, the blurs, the spaces where we find humanity if we’re only brave enough to look.