#OpenToBeSeen

Exposing AI Hiring Discrimination

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Stories Shared
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From time to time, it is useful to restate our most efficient ideas plainly, without the usual softening language of empathy, context, or lived experience. Clarity, after all, is a form of kindness. What follows is an exercise in that spirit, borrowing a few rhetorical habits from an 18th century pamphleteer best known for taking respectable arguments entirely seriously and seeing where they lead. The positions below are expressed with confidence, tidiness, and a sincere concern for productivity. If any of this sounds familiar, it may be because you have heard gentler versions of it before. If it sounds reasonable, that is also worth sitting with.

A Most Humble and Public-Spirited Proposal for the Relief of the Nation from the Nuisance of Job Seekers, and Other Idle Considerations

By Jonathan Swift (or so I am told)

I shall now therefore humbly propose my own thoughts, which I hope will not be liable to the least objection, being founded not upon compassion, experience, or evidence (those dangerous indulgences), but upon the far sturdier pillars of impatience, spreadsheet logic, and the confident opinions of people who are already employed.

I think it is agreed by all parties that the sight of a person seeking a job is at best uncomfortable, and at worst an accusation. Their resumes wander the land like vagrants, begging for attention, cluttering inboxes, and forcing respectable hiring managers to remember that fortune, rather than virtue, plays some role in human affairs. This alone is reason sufficient to do nothing whatsoever to assist them.

Therefore, I shall lay it down as a maxim: no help ought to be given to anyone looking for a job. For if employment were truly deserved, it would already have occurred. The invisible hand, having once mislaid them, clearly meant to do so. Any attempt to intervene is not charity but vandalism, scribbling over the clean, moral lines drawn by the market.

I submit these observations with the utmost seriousness, trusting that no reasonable reader would mistake their neatness for humanity, or their efficiency for wisdom.

As to the unemployed themselves, I confess I have long admired the admirable efficiency with which society has blamed them for their condition. For what is unemployment, after all, but leisure enjoyed incorrectly? The poor wretch without work is not a victim of cycles, offshoring, automation, or managerial fashion, but rather a moral lesson in motion. To employ such a person would be to interrupt the sermon.

I shall next address the matter of older workers, whose greatest sin is that they remember things. These persons, by the mere passage of years, have acquired wages inflated by experience, loyalty, and prior promises, none of which appear on a quarterly earnings call. They are said to produce less, though no one can precisely measure what they produce, nor can anyone remember how the systems work once they leave.

Yet we are assured, by those half their age and twice their confidence, that wisdom depreciates faster than software, and that the ideal workforce is one that resets every five years like a phone contract. This belief, I am told, is not bias but disruption.

Some have timidly objected that today's young managers, having established these principles, may one day themselves become older workers. But this is a groundless fear. For it is universally believed that when they age, circumstances will be entirely different. History, after all, has a well known habit of ending just before it becomes inconvenient.

I now come to the most elegant part of my proposal: Artificial Intelligence, that stainless and tireless entity which neither asks for raises nor calls in sick, and whose confidence grows in perfect proportion to its ignorance. We are assured it will add more to GDP than working adults ever could, which is a great relief, as it spares us the trouble of dealing with those adults at all.

Why endure the inefficiency of human judgment, training, morale, or meaning, when we may instead worship a system that produces charts? Charts, after all, have never unionized, never aged, and never asked what any of this is for.

I profess, in the sincerity of my heart, that I have not the least personal interest in promoting this necessary work, having no need of a job myself, and being content to watch from a safe distance as labor is optimized out of existence. My chief motive, as always, is the public good, particularly the good of those already doing well.

I shall conclude by humbly suggesting that if these proposals appear harsh, it is only because they are already widely believed. I have merely done the nation the service of writing them down.

A note for careful readers: this piece borrows the posture, not the conclusions, of a long tradition of satire. Disagreement is expected. Literal agreement would be… surprising.

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The above is satire.

The stories below are not.

Real Stories. Real People. Real Rejection.

Every day, qualified candidates are filtered out by algorithms that can't see them. Here are their stories.

I applied to 300+ jobs over 6 months. Not one interview. I changed my graduation year to appear younger and suddenly started getting callbacks. I'm 54 with 25 years of experience.

The ATS rejected me for a job writing the exact software I created at my last company. I literally built the system they wanted experience with.

At 52, I'm not old. I'm experienced. But the algorithm doesn't see my value. It sees a number. I deserve to know why I'm being filtered out.

99%
of Fortune 500 companies use AI screening
75%
of resumes never reach a human
40+
states with AI hiring bills introduced

The Evidence Is Clear

Peer-reviewed research and landmark lawsuits are exposing algorithmic discrimination at scale.

85%
AI prefers white names
0%
Black males favored vs white
1.1B
Applications in Workday lawsuit
$365K
First EEOC AI settlement

What We Demand

1
Transparency Candidates have the right to know when AI is evaluating them.
2
Explainability If AI rejects you, the company should be able to explain why. With evidence, not guesses.
3
Accountability Companies must be liable for discrimination by their algorithms, not shielded by vendor promises.
4
Due Process Every hiring decision should be auditable. Not with statistics, but with documented evidence for each candidate.

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