The Sankey diagram below reveals a striking pattern: while many methods of categorizing human populations—race, cranial measurements, and "feeblemindedness"—have faded into disrepute, one variable has persisted unchallenged: IQ. Highlighted in red, this enduring metric of intelligence illustrates how deeply rooted our obsession with measuring, ranking, and categorizing people by intellectual capacity has been.
IQ’s survival is no accident. It reflects a persistent belief that intelligence can and should be quantified—a single score that defines worth, potential, and societal role. Measured individually, IQ may offer insights into personal aptitudes or areas for development. But when metrics like IQ are used to categorize groups, the results have historically been deeply harmful. Once explicitly tied to eugenics and racial hierarchies, IQ measurement is now often framed as a tool to promote the elevation of categorized disadvantaged groups. This approach remains problematic: relying on a single metric to define value not only oversimplifies complexity but often reinforces the very biases it seeks to dismantle.By reducing diverse human experiences to a single score, we risk entrenching stereotypes and perpetuating systems of inequality under the guise of objectivity.
From IQ to AI: A Legacy of Categorization
The appeal of IQ lies in its perceived objectivity—a tidy numerical framework that seems neutral but continues to carry the weight of its history. The danger lies not in intelligence itself but in how we use it as a tool for ranking and dividing people.
This legacy persists as artificial intelligence emerges as the dominant force shaping societal systems. Modern AI systems operate on similar principles: categorizing, ranking, and filtering based on predefined metrics. Whether through recommendation algorithms or predictive models, these systems embed assumptions about human worth into processes that feel rational and impartial.
But what happens when AI inherits these flawed metrics? Without intervention, these systems risk amplifying biases rather than dismantling them. Worse, in the attempt to correct for historical injustices, AI systems may overcompensate, creating new hierarchies and exclusions that we are too blind to see today.
The Feedback Loop of Bias
AI systems, designed to maximize engagement and predict outcomes, mirror the logic of IQ: simplifying complexity into easily digestible categories. For example, recommendation algorithms on social media reinforce user preferences, creating echo chambers that validate existing beliefs. This feedback loop is not inherently malicious, but it is deeply consequential. It mirrors the historical misuse of statistical tools like IQ, which turned subjective biases into objective-seeming truths.
(To delve into the mathematical mechanics behind weighted feedback loops in social media algorithms and how they reinforce these patterns, click here.)
Consider this scenario:
- An individual seeks information about political movements.
- The AI system, recognizing their interest, prioritizes content that aligns with their views.
- Over time, the individual is funneled into ideological content, presented as fact-based and validated by "objective" data.
This cycle escalates radicalization and division, reinforcing the same categorical thinking that IQ once justified. Without safeguards, AI risks becoming the most powerful tool yet for perpetuating inequality and reinforcing dangerous ideologies.
Redefining Intelligence for the Future
We are at a crossroads. If we continue to treat intelligence as a fixed trait for categorization, we risk embedding not only yesterday’s prejudices but also the unseen biases of today—those we cannot yet recognize without the benefit of hindsight. These latent biases, left unchecked, may one day be viewed as the moral failures of our time, perpetuating divisions under the guise of progress.
But what if we reimagined intelligence as a tool for empowerment?
Intelligence is not an immutable trait like race or height. It’s a dynamic capability—a means for growth, collaboration, and innovation. By redefining intelligence in this way, we can break free from systems of categorization that divide humanity.
This requires a fundamental shift:
- Decoupling intelligence from human worth. Intelligence should guide personal growth, not dictate societal hierarchies.
- Designing AI to focus on individuality. Models must move beyond group-level assumptions to recognize unique contexts and complexities.
- Shifting from ranking to enabling. Intelligence should unlock potential, not gatekeep opportunities.
The Philosophical Foundations of Control
This challenge is deeply philosophical. Kantian idealism teaches that reality is shaped by the mind. Figures like Galton, Pearson, and Fisher operationalized this belief, transforming subjective judgments into data-driven categorizations. Modern AI systems risk perpetuating this same logic, turning personal biases into systemic truths.
Without safeguards, AI can create powerful feedback loops that manipulate perception and entrench divisions. This isn’t a theoretical risk; recommendation systems already escalate polarization and extremism. As AI becomes more advanced, these dangers grow exponentially.
The stakes are clear: Will AI empower individuals to think critically and engage with diverse perspectives, or will it reinforce the worst of human bias under the guise of neutrality?
The Urgent Need to Rethink Intelligence
To avoid repeating history, we must rethink intelligence—not as a tool for division but as a catalyst for human advancement. AI systems should challenge assumptions, foster critical thinking, and empower individual growth. This demands:
- Building models grounded in reality, not subjective judgments.
- Designing AI to disrupt harmful feedback loops.
- Embedding safeguards to prevent escalating biases.
If we fail to act, AI won’t just automate bias—it will amplify it, turning personal prejudice into systemic control. But if we succeed, we can redefine intelligence as a force for empowerment, breaking free from the cycles of exclusion and hierarchy that have shaped its past.
Continue reading: Conclusion of This Project