The Right to Human Judgment - Is this the Way?

When Algorithmic Efficiency Meets the Limits of Unaccountable Decision-Making (an exercise in AI policy)

This argument didn’t start with AI. It started with a conversation about doors.

Years ago, someone who spent their career thinking about how user interfaces need to accommodate a wide range of physical realities taught me something I didn’t fully understand at the time. The point wasn’t that some people need ramps instead of stairs. The point was that the people who designed the building never thought about who couldn’t get in. The exclusion wasn’t malicious. It was structural. The system was built for the average, and the average isn’t everyone.

I’ve been thinking about that conversation ever since AI started making decisions about people’s lives.

The Fallacy We’ve Accepted

There is a phrase you will hear constantly in discussions about AI governance. Human-in-the-loop. It sounds responsible. It implies oversight. It is largely theater.

Here is what human-in-the-loop actually means in practice: an AI system does 99% of the cognitive work, generates a high-confidence output, and then routes that output to a human reviewer who has no independent context, insufficient time, and no realistic ability to contest a machine’s conclusion. The human clicks approve. The decision stands.

This isn’t oversight. It’s a signature on a document you didn’t read.

The deeper problem is structural, not motivational. When a system is designed to hand a human the AI’s conclusion and ask them to verify it, you haven’t created a check. You’ve created a bottleneck with a rubber stamp at the end. The human isn’t deciding. They’re absorbing the AI’s decision and adding their name to it. That matters enormously when the decision is wrong.

And the AI will be wrong. Not randomly. Systematically.

The Proof Problem Nobody Wants to Talk About

Here is a question that doesn’t get asked often enough: How do you prove an AI isn’t biased?

The standard answer involves model documentation, training data disclosures, weight transparency, audit trails. These are useful technical artifacts. They are also produced and interpreted by the same parties who built and deployed the system. An AI company telling you their model isn’t biased, and then offering their own documentation as evidence, is structurally similar to a defendant offering their diary as proof of innocence. The verification mechanism is controlled by the interested party.

There is no independent, universally accepted methodology for proving the absence of AI bias. That is not a technical problem waiting to be solved. It is an epistemological problem that no amount of engineering will fully resolve. The claim of neutrality is itself unverifiable by the parties most affected by it.

This matters because it makes the bias question circular in exactly the way that due process law cannot tolerate. Courts and legislatures have spent centuries building frameworks to manage conflicts of interest. The core principle is simple: you cannot be the judge of your own case. That principle is about to need an update.

The Jury Wasn’t Built to Be Unbiased

Before going further, let me address the obvious counter-argument.

“Humans are biased too.” Yes. Correct. That is not the point.

The jury system was never designed to produce unbiased verdicts. It was designed to produce accountably biased verdicts: verdicts shaped by peers, by conscience, by community standards, by the specific facts of a specific life. That is a completely different thing from algorithmic neutrality. Nobody has ever seriously proposed replacing juries with sentiment analysis models. Ask yourself why, and you’re already most of the way to understanding this argument.

Human bias in adjudication is visible and arguable on appeal. It is subject to recusal and challenge. It can be named, questioned, and overturned. Sometimes it is deliberately preserved as a democratic value: prosecutorial discretion, judicial sentencing latitude, equitable relief in contract law. These aren’t flaws in the system. They’re valves that allow human moral reasoning to override mechanical rule application when the stakes demand it.

AI bias is different. It’s opaque by default. It presents itself as neutral when it isn’t. And it fails in a specific way that human bias does not: it fails statically. A biased human reviewer makes an idiosyncratic mistake: variable, unpredictable, subject to the randomness of different adjudicators on different days. A biased AI makes the same mistake across every loan application, every resume screen, every benefits determination that passes through its logic. One error, multiplied by a million, at machine speed. The predictability of that failure is what makes it a systemic danger in a way that human error, for all its flaws, is not. Variable error can be managed. Static error at scale cannot.

The Right to Human Judgment: A New Legal Standard

What I am proposing in this thought exercise, and what I believe states and eventually federal law will begin to codify, is not a ban on AI. It is a separation of powers for decision-making itself.

Think about how a courtroom works. An expert witness takes the stand. They present findings, data, analysis. They are explicitly prohibited from directing the verdict. The jury weighs that evidence independently, alongside everything else in the record. The human decision-maker isn’t reviewing the expert’s conclusion.

They are receiving it as one input among many and making an independent determination.

That is the model. The AI is a witness, not a judge.

An AI system can legitimately establish facts: this applicant has missed three payments, this resume lacks a specific credential, this claim was filed 47 days after the incident. What it cannot do, and what it should have no legal authority to do, is conclude “therefore deny.” The facts are data. What they mean in the context of a human life is a judgment call. Those are not the same thing.

The legal standard that captures this most precisely is de novo review. Not “did the AI make a reasonable determination?” but “what does a human evaluator conclude when they examine the same evidence independently, without the AI’s output as a starting point?” De novo review already has precise meaning in law. It means the reviewer starts fresh, unanchored from what came before. That standard isn’t philosophically novel. It’s enforceable. A legislature could write it into statute tomorrow.

Where the Law Will Intervene

The specific transactions where this right will first be codified aren’t hard to predict. They share a common structure: the decision is difficult or impossible to reverse, the affected party has limited visibility into why it was made, and the stakes involve livelihood, liberty, shelter, or family integrity.

The likely first wave:

• Credit and finance: The right to present financial context to a human when an algorithm issues an adverse action. Not a request for reconsideration of the AI’s conclusion, but an independent human determination on the merits.

• Employment: The right to human evaluation in hiring and termination, removing resume-screening bots as final gatekeepers. Being deactivated from a gig platform without a human ever having reviewed your situation is a termination without a decision-maker.

• Housing: Algorithmic tenant screening is already being litigated in several cities. The practice of screening out applicants based on pattern-matching against population data, rather than individual circumstances, is the ramp-removal problem in digital form.

• Health: The right to a human-led diagnostic or treatment pathway, particularly when AI-driven clinical protocols deviate from what a patient’s specific history would suggest.

• Benefits and licensing: Administrative determinations about food assistance, disability benefits, permits, and professional licenses are the quietest battleground and probably the most consequential for the people affected.

• Government contracting: When an AI system scores a small business out of a government bid, a company can be destroyed without a single human ever evaluating their actual work. This category is almost entirely undiscussed.

  
  

Notice what these share. The irreversibility criterion may ultimately be cleaner legislative language than any enumerated list of categories. Technology will always outpace a list. What will not change is the principle: when a decision shapes a life in ways that cannot easily be undone, a human being must own it.

The ADA Analogy Is Not Decorative

The Americans with Disabilities Act established a legal concept that this movement will borrow directly: reasonable accommodation.

The ADA didn’t ask whether accommodating physical access was economically efficient. It established that equal access was a right, and that institutions were obligated to provide it regardless of cost. That framework will be extended. Just as the law requires reasonable accommodation to ensure equal access to physical environments, it will evolve to require reasonable human adjudication to ensure equal access to economic and legal outcomes.

The reason is the same in both cases. Systems built to optimize for the average will always screen out outliers. People with unusual financial histories, complex medical situations, non-standard employment patterns, or circumstances that don’t fit the training data are not edge cases to be discarded. They are the people the law is most obligated to protect. A human-led determination is the ramp. The AI is the building it gets you into.

The economic counter-argument will arise immediately. Human review at scale is expensive. The answer to this is the same answer we give when someone argues that public defenders are too costly, or that jury trials slow down the courts. Due process doesn’t have a cost-benefit exception.

And there is a second answer. Institutions adopting AI because it reduces operational costs have already generated the fiscal headroom to fund human review mandates. You cannot simultaneously argue that AI saves money and that you cannot afford the oversight it requires. Those two positions cancel each other out. Beyond that: mandates create markets. If human-led determination becomes a legal requirement, the industry will build efficient infrastructure to deliver it, the same way e-discovery tools emerged to manage legal document review costs.

What Has Already Failed

This argument has a history worth acknowledging.

HITL requirements, algorithmic accountability laws, and bias auditing frameworks have been tried in various forms. Most have failed to produce meaningful change, not because the instinct was wrong, but because they were framed as technical safeguards. Technical safeguards get optimized away when they become inconvenient. They get gamed, watered down, rubber-stamped.

The Human-Led Determination framework is different in a specific way. It is a rights claim, not a compliance requirement. Rights claims require affirmative legislative action to remove. Agency as a legal concept has centuries of jurisprudence behind it. You cannot optimize away a right the way you can optimize away an audit trail.

The efficiency lobby will attempt one more move: arguing that they can simply build AI to mimic human adjudication. Train a model on how human reviewers decide, deploy it, call it human-led. This is the same rubber stamp with better branding. The distinction that legislation will need to draw, and that courts will eventually enforce, is this: a human adjudicator is a person with the time, authority, and genuine latitude to deviate from what the system recommends. Not a person following an AI prompt while technically holding the pen. The deviation has to be real. If the human cannot say no to the machine, they are not leading the process. They are finishing it.

The International Dimension

The EU and the United States are the most likely first movers, given existing regulatory infrastructure. The EU AI Act already establishes risk categories. State-level activity in the US is accelerating. But the scope of this article is analysis, not comparative law. Other jurisdictions will find their own path to the same pressure point.

What is worth noting is that GDPR began as a regional framework and became a de facto global standard because multinationals found it easier to apply one compliance model universally. Human-led determination mandates could follow the same path, not through treaty, but through market pressure. The mechanism doesn’t matter. The direction does.

Reclaiming Agency

I am not arguing this is the right outcome. I am arguing it is the inevitable one.

The goal is not to slow AI down. AI is genuinely better than humans at pattern recognition, data synthesis, and analytical processing. Use it for those things. Use it aggressively.

The goal is to stop pretending that analytical output and adjudicative judgment are the same thing. They are not. They never were. We just hadn’t built systems fast enough to make the confusion consequential.

What is coming, and what has to come because the alternative is unacceptable in a society governed by law, is the formal institutionalization of human moral and contextual judgment as a legal requirement in the decisions that shape individual lives. Human-Led Determination will become both a competitive differentiator and a baseline legal standard. In a world of automated logic, the human decision-maker doesn’t become obsolete. They become the source of legitimacy.

The building still needs a ramp. That was true before AI, and it will be true after every regulatory framework we try to build around it.

The question is whether we design it in deliberately, or wait until someone who needed it falls at the door.

Joel Fogelson writes about AI, IP, and business at TakemyIP.net. This article reflects analytical frameworks developed through practice and observation. It is not legal advice or regulatory guidance. Joel likes AI, so don’t be a hater.