The Hidden Flaws of Reasonable Decisions

“Your ‘reasonable’ decision is completely wrong. What is your framework to correct your own biases?”

That accusation might sound harsh, but in today’s rapidly evolving landscape, the question “Is this reasonable?” has never been more relevant—or more misleading. As technology continues to reshape the way we work and think, we’re constantly being nudged to ask ourselves what constitutes “reasonable” solutions. And for better or worse, that question isn’t going away anytime soon.

Large Language Models (LLMs) have made impressive strides in evaluating “reasonability.” They can analyze scenarios, interpret patterns, and churn out responses that align with existing societal norms. On the surface, these answers often feel logical, appropriate, and…well, reasonable.

Yet correctness is a moving target. Culture, context, and lived experiences all shape how we define “right” versus “wrong.” So a solution might be perfectly reasonable but still miss the mark in a broader—or deeper—context.

As LLMs get better at offering rational-seeming responses, we enter a tricky space. On one hand, having technology that can provide well-structured, coherent solutions is an incredible asset. It can guide decisions, spark new ideas, and streamline problem-solving.

On the other hand, relying solely on “reasonability” without ever questioning the hidden assumptions or biases can lead us astray. Think about it this way: if you only ever ask yourself, “Is this solution logical?”, you might never realize that the entire premise or data set you started with was flawed in the first place.

Uncovering Biases

So how do we guard ourselves against this trap? It’s not enough to just nod along when something seems to “make sense.” We need to peel back the layers and figure out why it seems that way. Which assumptions did we take for granted? Whose perspective did we overlook?

Here’s where it gets personal. Each of us has biases—some conscious, many unconscious. LLMs inherit their own set of biases from the data they’re trained on. Our role is to critically evaluate the reasoning behind every decision, whether human or AI-generated.

This is what you can do:

1. Spot Your Starting Point

Before jumping to a conclusion, pause and ask: Which assumptions did I bring to the table? Make a habit of identifying how your own background, culture, and experiences might influence what you consider “reasonable.”

2. Interrogate the Source

Whether you’re getting a suggestion from an LLM or a colleague, question where the information came from. Does this source have inherent biases? Is the data complete and diverse enough?

3. Ask the Inconvenient Questions

Don’t let “it makes sense” be your final verdict. Challenge the logic: What counter-arguments exist? What would it take for this to be wrong? This step often reveals hidden gaps and assumptions.

4. Invite Contrasting Perspectives

Seek out opinions different from your own—intentionally. Whether it’s through diverse teams, peer reviews, or simply talking to people outside your field, fresh angles can expose blind spots.

5. Iterate and Reflect

Bias correction is not one-and-done. It requires continuous learning and adaptation. Revisit past decisions, note where you went wrong (or right), and adjust your thinking accordingly.

In an age where AI can readily serve up seemingly “reasonable” justifications, the real skill is learning how to look deeper. What is your framework for assessing and trusting the reasons behind decisions? How are you ensuring that your pursuit of “reasonable” doesn’t overshadow the pursuit of truth?

The bottom line? “Reasonable” and “correct” are not always the same thing. Staying mindful of that gap—and diligently working to bridge it—is how we evolve from passive recipients of logic to active stewards of deeper understanding. That’s how we become truly equipped to navigate a world where “reasonability” is offered up at the click of a button, but genuine insight remains a rarer find.

Original Post: linkedin.com