By Mariana Abdala
AI is becoming deeply embedded in modern product experiences. It helps shape recommendations, can automate decisions, and can influence how customers interact with digital systems. Interestingly, at PAS we see embedded AI experiences most often utilized t in products that are internal tools or platforms, which means that bias, opaque reasoning, loose data governance, or unintended outcomes can put an organization at risk and can erode trust. This is why we’re seeing the evolution of the topic of AI Ethics. The ethics of AI is no longer a research topic. It is a core product competency.
As Product Managers, we know that shrewd product decisioning requires more than accuracy and efficiency, and that goes for any type of product development. Even more so with AI-powered products, Product Managers need to be proficient in how AI systems learn, how they benefit the business vs. the customer, and who or what they may unintentionally exclude. Ethical product management starts with the recognition that teams are not just shipping code. They are shaping behavior. They are building systems that will make choices on behalf of customers, sometimes without anyone noticing. This high level of awareness in Product is especially critical in highly regulated industries like Finance, Healthcare, and the Government sector, industries in which most of our own clients operate.
AI and Bias
Bias does not appear only in data. It appears in assumptions about the problem, the framing of success metrics, and the shortcuts teams take under pressure. Ethical review checkpoints help teams pause and interrogate those assumptions. They create intentional friction, the healthy kind, that protects long-term trust even when short-term deadlines feel urgent.
An effective ethical review process does not need to be heavy. It can be a short ritual at key stages of development. Here’s a rough framework of what we encourage our teams to ask:
What decision is the AI making, and for whom?
What data is it learning from, and what might be missing?
What harm could occur if the model is wrong?
What groups might experience the outcome differently?
How will we monitor behavior once the feature is live?
These questions shift the conversation from technical feasibility to human impact. They help teams see hidden risks before they reach customers. They also make the product more resilient. When you ask better questions, you discover blind spots early enough to act on them.
Diverse perspectives strengthen ethical decision-making. In our work, we’ve observed that Product managers, designers, engineers, and data scientists might share similar backgrounds and mental models, and that similarity creates blind spots. In our advisory work, we highly encourage bringing in voices from customer support, legal, operations, or community teams to create a broader lens. These groups see how features behave in real life, not only in test environments. Their insight often reveals unexpected consequences or opportunities for clearer communication, especially if the products in your portfolio are being utilized by these stakeholders.
AI & Transparency
Transparency is the foundation of ethical AI. I believe this is why Google has taken very proactive steps to make Gemini more balanced, honest, and not committing to a particular point of view or recommendation.
Customers do not need to understand every technical detail. They need to understand what the system is doing on their behalf. Clear explanations build confidence. Confusing or hidden behavior erodes it. A simple description of how recommendations work, how decisions are made, or what data is used can prevent misunderstandings and reduce fear.
When AI influences decisions with financial, health, or safety implications, transparency becomes even more important. Customers should know when they are interacting with automation and what options they have for control or appeal. Giving users a way to override, correct, or question AI decisions strengthens trust. It also improves the model. People provide context that data alone cannot capture.
The Future of Product with AI
The future of product work will involve more automated reasoning, more predictive systems, and more invisible decision pathways. The organizations that succeed will be the ones that balance ambition with responsibility and prudence. It is not necessary to move at lightning speed if you don’t see where you’re going to end up using AI functionality or integrations in your products. We’ve seen that the most optimally performing product teams incorporate ethical review checkpoints, diverse perspectives, and transparent communication to create that balance and build trust with their business counterparts. They ensure that AI serves as an extension of human judgment, not a replacement for it.
AI can scale decisions. Ethics ensures those decisions scale well. When teams design with care and communicate with clarity, they build products that customers can rely on.
