The Algorithm and the Overlooked: How AI Deepens Old Inequalities

What Adler, McLuhan, and Merton would say about the machine we’ve built — and who it’s really for

Artificial intelligence does not hurt everyone equally. Where it causes the most harm is in the systems that were already failing people — hiring, housing, policing, benefits, healthcare, surveillance. In each of these domains, AI has a troubling tendency to codify existing inequalities. The deeper problem, read through the work of Mortimer Adler, Marshall McLuhan, and Thomas Merton, is not simply “bad technology.” It is a distorted view of the human person — one in which efficiency and optimization have quietly outrun justice, dignity, and solidarity.

Cause and Effect

The causes are familiar by now: biased training data, opaque automated decisions, concentrated power in the hands of a small number of firms and agencies, and relentless pressure to deploy AI wherever it is cheaper or faster than a human being. These causes produce concrete harms — discriminatory screening in housing and lending, overpolicing through predictive systems, wrongful denial or indefinite delay of public benefits, erosion of privacy, and a widening gap between those who can adapt to an AI-saturated economy and those who cannot.

For people without much social or economic power, the damage rarely arrives as a single dramatic failure. It arrives as a pattern — a long string of small exclusions that, taken together, push someone deeper into precarity. The algorithm doesn’t slam the door. It just keeps failing to open it.

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THREE LENSES

What the Thinkers Tell Us

MORTIMER ADLER — ON HUMAN DIGNITY

Adler built his philosophy on a single, demanding premise: human dignity is universal, grounded in what we share as persons — not in our skill, speed, productivity, or measurable intelligence. That principle becomes urgent in an age of AI. If we allow algorithms to become the measure of a person’s worth, we will inevitably treat the weak, the disabled, the poor, and the digitally excluded as less significant. Adler also presses a harder question: are our schools and public institutions forming people capable of genuine judgment, or are they simply conditioning them to slot into an automated economy?

MARSHALL MCLUHAN — ON THE ENVIRONMENT WE DON’T SEE

McLuhan redirects our attention from the shiny tool to the surrounding environment it creates. AI is not merely a device; it is a new media ecology that reorganizes perception, attention, and social relationships. In his framework, the “ground” matters as much as the “figure” — and the ground of AI quietly normalizes surveillance, algorithmic dependence, and individualized information bubbles, even when the product on the surface looks convenient and neutral. The poor and marginalized are especially exposed here because they have the least power to opt out of the systems that classify, sort, and score them. They cannot simply log off.

THOMAS MERTON — ON THE SOUL OF A TECHNOLOGICAL CIVILIZATION

Merton warned that technical progress becomes a form of cultural disintegration when it is not governed by ethics and humane limits. He also insisted — well ahead of most of his contemporaries — that injustices like racism and poverty are systemic, not merely personal failings. That insight fits the way AI can intensify existing injustices rather than dissolve them into neutral efficiency. From a Mertonian perspective, the response is both contemplative and political: recover interior freedom, resist the spell of expediency, and judge every technology by whether it serves love, justice, and the genuine good of the people most vulnerable to its failures.

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What a Good Response Looks Like

Getting this right requires more than technical fixes. Any serious response to AI-driven injustice must begin with community participation — especially from those most likely to be harmed — before systems are ever deployed. Beyond that, it demands transparency, meaningful human review, tightly defined use cases, and a strong presumption against automating decisions that affect people’s rights, housing, income, or physical safety.

In Adler’s terms, the goal is a public culture that forms judgment. In McLuhan’s work, it is awareness of the full environment, not just the interface. In Merton’s, it is a spirituality and a politics that, stubbornly and together, refuse to sacrifice the poor on the altar of efficiency.

QUESTIONS TO TAKE AWAY

1. When an algorithm makes a consequential decision about a person’s life, who bears moral responsibility — the engineer who trained it, the agency that deployed it, the policymaker who authorized it, or some combination of all three?
2. McLuhan argued that we shape our tools and then our tools shape us. What habits of mind — about fairness, judgment, and human worth — are we slowly forming by delegating more decisions to machines?
3. Adler insisted that every person possesses dignity independent of their productivity. Does the design of our AI systems reflect that belief — or does it quietly contradict it?
4. Merton saw contemplation and justice as inseparable. Is there a form of “digital contemplation” — a practiced, intentional attention — that citizens and technologists alike need to cultivate right now?
5. If the communities most affected by AI systems were given genuine authority over their design, what would those systems look like differently?