VICTOR KIANI

Case StudyGrade: A

When AI Meets Ethics

I built an AI ethics consultant to navigate a hospital's moral dilemma. Then I argued with its recommendations.

Course
Business Ethics
Code
LRMS1-UC
Institution
New York University
Completed
December 2025

Scenario

A 450-bed Chicago hospital. One uncomfortable question.

100,000 patients a year. An access satisfaction score sitting at 60%. Leadership wanted it higher, and they had a proposed solution in mind:

“Should we limit appointments for uninsured patients so that insured patients, especially Medicare patients whose satisfaction scores affect our funding, can be seen faster?”

Restrict how many appointments uninsured, self-pay patients could book. Lift the numbers that mattered to the board. The Director of Patient Access pushed back. It felt wrong. But “feeling wrong” isn't an argument in a boardroom.

I was asked to find one.

Approach

Engineering an AI ethics consultant from scratch

Instead of analyzing the case alone, I wanted to test a question that's becoming increasingly urgent: can AI help humans make better ethical decisions, or does it just automate our biases faster?

I built a persona from the ground up: Dr. Amina Solberg, a virtual healthcare ethics consultant on Claude Opus 4.5.

Persona architecture

Identity: Dr. Amina Solberg
          AI Healthcare Ethics & Innovation Consultant

Expertise Layers:

├── Bioethics Foundation
│   ├── Autonomy (patient choice)
│   ├── Beneficence (promote well-being)
│   ├── Non-maleficence (avoid harm)
│   └── Justice (fair distribution)
│
├── Business Ethics Integration
│   ├── Stakeholder theory
│   ├── Corporate social responsibility
│   └── 7-step decision-making model
│
└── Healthcare Operations
    ├── Scheduling systems
    ├── Triage protocols
    └── AI/data ethics in clinical workflows

What the AI got right

Where Dr. Solberg earned her keep

It reframed the problem

Leadership had framed this as uninsured patients vs. insured patients.

Dr. Solberg reframed it as: Is the access problem actually caused by uninsured patients, or by systemic capacity issues?

That matters. The proposed solution assumed uninsured patients were “crowding out” others. But what if the real problem was inefficient scheduling, high no-show rates, or insufficient clinic hours?

It generated creative alternatives

Instead of accepting the binary choice, the AI produced seven options:

A
Implement the restriction (cap uninsured appointments)
B
Maintain status quo
C
Expand capacity (hours, providers, rooms)
D
Improve scheduling efficiency (no-show management)
E
Create differentiated access pathways
F
Proactive insurance enrollment assistance
G
Hybrid approach combining D, C, and F

The AI recommended Option G: addressing the problem without discrimination.

It connected principles to stakeholders

Justice
Uninsured patients lose access based on ability to pay, not clinical need.
Non-maleficence
Delayed care leads to disease progression, ER overcrowding.
Autonomy
Restricting appointments removes patient choice.
Beneficence
Leadership's goal helps some patients at the expense of others.

Where I disagreed

The blind spots in its reasoning

Blind spot

Coercion risk

The AI recommended proactive insurance enrollment assistance: helping uninsured patients sign up for Medicaid or marketplace plans. On the surface, this seems beneficial. I pushed back:

“Offering enrollment assistance could unintentionally pressure patients into coverage decisions that don't align with their financial situation or values.”

Blind spot

Feasibility optimism

Dr. Solberg placed significant weight on capacity expansion. The problem? Hospitals can't expand overnight.

“Relying too heavily on expansion reflects optimism rather than a realistic, evidence-based step.”

Recommendation

Reject the restriction

Limiting access based on insurance status violates justice and risks non-maleficence. Instead, a four-stage path:

  • Immediate

    Scheduling audit

    Identify actual causes of access delays; implement no-show management and standby lists.

  • Short-term

    Clinical triage

    Prioritize urgent cases regardless of payer status.

  • Medium-term

    Capacity evaluation

    Evaluate expansion based on audit findings.

  • Ongoing

    Quarterly reviews

    Track satisfaction scores, wait times, and payer mix trends.

Takeaway

What the project taught me

  • AI as scaffold, not authority

    Dr. Solberg helped me see the problem from multiple angles and generate options. But AI can't assess real-world feasibility or detect subtle coercion.

  • The human role isn't going away

    The most valuable part wasn't the AI's output. It was my critique. Knowing when to agree, push back, and synthesize: that's the skill that matters.

  • Ethics is operations

    The best solutions aren't “more ethical” in a pure sense. They're ethically sound and practically achievable.