At first glance, the title Social and Ethical Concerns in Analytics appears innocuously simple and focused; however, it’s anything but. Analytics is a vast and sprawling domain, encompassing an array of concepts, disciplines, and technologies. Its reach extends across nearly every modern buzzword circulating in the tech industry, from Big Data and artificial intelligence to algorithm development and predictive modeling.

In a recent graduate-level course on this topic, we were assigned two books that purported to be tell-all exposés on the ethical and moral dilemmas embedded within the analytics landscape: Everybody Lies (Stephens-Davidowitz, 2017) and Weapons of Math Destruction (O’Neil, 2016). Both works carried a strong pretense of authority, according to their authors, and I’m certain they were selected for the course precisely because of those pretenses.

Both works begin with legitimate analytical case studies, baseball in Weapons of Math Destruction (O’Neil, 2016) and the NBA in Everybody Lies (Stephens-Davidowitz, 2017), but quickly spiral into conjecture framed around systemic racism, bigotry, and other ideological buzzwords used to convict the innocent and shut down dissent. The subtle shift from rigorous analysis to rhetorical positioning was, I suspect, missed by many readers. Yet the lack of causal rigor behind these sweeping claims left me having to force myself through the ideological drivel.

Both authors begin with compelling sports analytics using familiar, data-rich domains to establish credibility and draw readers into their broader ethical critiques. But this rhetorical launchpad quickly gives way to sweeping claims about racism, bigotry, and systemic harm lurking in every corner of society. Works that claimed to illuminate hidden truths instead revealed their own ideological bias, violating the very standards of rigor they purported to uphold. If these authors were truly committed to ethical clarity, why did they systematically pass off findings that lacked causal evaluation, let alone demonstrable correlation, as fact?

As I recognized the subtle ethical and rhetorical failures in these works, I couldn’t ignore their timing. Both books emerged during a pivotal shift in American leadership, an administration that ran on and began executing a platform of national prioritization, putting America and American citizens first. The authors positioned their critiques as counterweights to this shift, framing analytics as a battleground for moral and ideological resistance. But in doing so, they embraced narrative convenience to promote ideological rhetoric, violating the very standards they claimed to defend.

A case in point is that O’Neil’s pivot to recidivism analytics includes a striking claim: that certain intake questions, such as “How many interactions have you had with law enforcement?” (O’Neil, 2016), were crafted by racists and bigots to ensure longer prison terms for Black inmates. But this attribution of intent lacks causal rigor. It’s far more likely that such questions were perceived as predictive indicators, albeit ones weighted too heavily and applied without sufficient contextual nuance, and something hard to find these days, common sense.

For example, individuals living in rural areas, regardless of race, are statistically less likely to have frequent interactions with law enforcement. In contrast, those from high-crime urban environments may report more encounters, not because of personal behavior, but because of systemic policing density. The disparity in responses reflects geographic and structural realities, not necessarily institutional and systemic racial bias. As human beings, it is not our environment that defines us; it is our choices.

This truth is exemplified by individuals like Dr. Ben Carson, who grew up in a high-crime urban environment but chose a path of discipline, education, and personal responsibility, ultimately rising to national prominence as a neurosurgeon and public servant. Similarly, Clarence Thomas was raised in poverty in the segregated South, and Thomas Sowell overcame early hardship in Harlem after being orphaned and dropping out of high school.

Each of these men emerged from structurally disadvantaged environments, yet rose to prominence through personal agency, intellectual rigor, and an unwavering commitment to self-determination. Their lives challenge the deterministic narratives often embedded in ideological beliefs regarding the environment from which they rose, and this reminds us that human agency remains a powerful counterweight to statistical generalization. Their choices are what drove them to greatness.

If the intent behind assigning these two books was to highlight ethical and moral concerns in analytics, not only in how predictive models are applied within systems like criminal justice, but also in how analytics can be weaponized to advance political ideology without causal rigor, then that goal was achieved.

However, the intended audience for these works seems less like the analytically minded and more like casual readers seeking affirmation of their existing belief systems. That renders these books less as scholarly critiques and more as ideological fodder. Tools used to reinforce partisan narratives rather than to illuminate truth. In that light, they risk serving not as instruments of ethical inquiry but as catalysts for division, unrest, and rhetorical escalation.

One must ask: who stands to gain from such incompetent drivel?

Either way, the books did help the class to consider the potential repercussions of work in these type of technical areas, and made for some interesting discussions. They served the purpose of making one cognizant.

The class was great, the nefarious use of ideological inference was disappointing.

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References:
O’Neil, C. (2016). Weapons of math destruction: How big data increases inequality and threatens democracy. Crown Publishing Group.
Stephens-Davidowitz, S. (2017). Everybody lies: Big data, new data, and what the Internet can tell us about who we really are. Dey Street Books.