Is Your AI Fair? Lessons from Healthcare on Building Trustworthy Tech

Ever wonder if the AI you're using is truly impartial? Whether it's recommending a movie, shortlisting a job applicant, or even influencing a medical diagnosis, the question of fairness is one of the most important challenges of our time.

While AI holds incredible promise, a recent review in Modern Pathology on AI in medicine gives us a powerful reality check. It shows us that if we aren’t careful, we can build AI systems that inherit some of our worst human biases. The lessons learned in the high-stakes world of healthcare are essential for anyone wanting to create, or simply understand, ethical AI.

The Core Problem: Garbage-In, Garbage-Out

The single biggest challenge is bias, and it often starts with the data used to train the AI. The principle is simple: "Garbage-In, Garbage-Out." If you train an AI on flawed or incomplete information, its results will be flawed and incomplete.

The article gives a stark example from prostate cancer diagnostics. Different Gleason patterns, which are used to grade the cancer, have different prognoses. If an AI model is trained on a dataset where the most dangerous pattern (Gleason pattern 5) is rare, it may not learn to detect it effectively. When this model is then used on a patient population where that pattern is more common, it could fail to spot the aggressive cancer, leading to under-grading and improper treatment.

This isn't just a data problem. Bias can creep in during an AI's development or even in how we interact with it. For instance, a doctor might trust a flawed AI's recommendation too much, a phenomenon called "automation bias."

The Four Pillars of Building Ethical AI

To combat these issues, the researchers highlight four ethical pillars, adapted from medicine, that should guide all AI development. For your AI literacy site, think of them this way:

1. You're in Control (Autonomy): You should have the right to decide how your personal data is used to train an AI.

2. Do Good, Avoid Harm (Beneficence & Non-maleficence): The AI's potential benefits must always outweigh its risks.

3. Fairness for All (Justice): An AI’s benefits shouldn’t only be for a privileged few. It must be designed, tested, and deployed equitably so it doesn't disadvantage marginalized groups.

4. Someone is Responsible (Accountability): Developers and users must be accountable for an AI's performance and impact, ensuring it is designed and operated ethically.

A Path Forward: How We Can Build Fairer AI

So, how do we fix this? The answer isn't a single switch, but a constant, vigilant process. Experts recommend a "bias mitigation" approach that covers the AI’s entire lifecycle, from its first line of code to its daily use. This includes everything from carefully balancing training data to continuously monitoring the AI for unfair patterns after it's deployed. 

One of the most powerful tools in this effort is the FAIR Principles. This is a framework designed to make the data and models used in AI:

• Findable: Researchers can easily discover diverse datasets to train and test their models.

• Accessible: Data is made available to a wide range of stakeholders so it can be scrutinized for bias.

• Interoperable: Data can be integrated and analyzed across different systems, allowing for better comparisons and validation.

• Reusable: Data and methods are well-documented, so findings can be replicated and validated, building trust through transparency.

By embracing principles like FAIR, we move away from "black box" AI and toward systems that are transparent, accountable, and trustworthy.

The lessons from medical AI are universal. Whether the stakes are a life-saving diagnosis or a fair loan application, our goal must be the same: to build AI that serves everyone, equitably. By prioritizing these ethical frameworks, we can harness the incredible power of AI to create a more just and fair world for all.

Reference

Hanna, M. G., Pantanowitz, L., Jackson, B., Palmer, O., Visweswaran, S., Pantanowitz, J., ... & Rashidi, H. H. (2025). Ethical and bias considerations in artificial intelligence/machine learning. Modern Pathology, 38(3), 100686.