Bias in AI—The Conversation Pharma Can’t Skip

AI is a mirror. It reflects the data it’s trained on—and if that data is biased, the consequences can be serious.

In healthcare, this isn’t just a technical issue. It’s an ethical imperative.

During my Harvard training, one principle was crystal clear:

Ethical implementation is non-negotiable.

If our datasets underrepresent certain populations, our algorithms will underperform—or worse, mislead. That means tools that work well for some patients but fail others. In a field where equity is already fragile, we cannot afford to widen the gap.

Here’s what ethical AI in Pharma demands:

• Inclusive data: We must actively seek out and include diverse patient populations.

• Transparent design: Black-box models erode trust. Stakeholders need to understand how decisions are made.

• Continuous testing: Equity isn’t a one-time checkbox—it’s an ongoing commitment.

Trust is pharma’s most valuable asset. If we lose it, we lose everything. How is your team tackling bias and equity in AI? Let’s share ideas and build better.

Author: Agata Kinga Kaczmarek - The Health Tech Advocate