Author: Itish Garg
A closer look at the growing gap between how accurate our AI systems have become, and how little we understand about the choices they make.
A while back, I came across a story about a major U.S. bank using an algorithm to set credit limits. Two people with identical financial footprints ended up with wildly different limits. When one of them called to ask why, nobody at the bank could answer. Not because they were dodging the question, but because they honestly didn’t know. The system made a choice that even its creators couldn’t trace.
That story stuck with me. It points to a growing problem in tech. Over the past decade, we got very good at building systems that are accurate. But we are still surprisingly bad at making them understandable. As AI takes over more real-world decisions, that gap is becoming impossible to ignore.
INSIDE THE BLACK BOX
Most automated tools decide who gets a loan, whose resume reaches a recruiter, or which patient gets prioritized in an ER. Many run on deep learning. At a basic level, these models are just massive webs of numbers. They are fine tuned over millions of training examples until outputs look right.
Nobody sits down to code straightforward rules like if income is high and debt is low, approve.
Instead, the model spots its own patterns. It buries those insights across thousands or millions of tiny weights. The result is a system that makes decisions in a way that bears almost no resemblance to human logic.
That is the classic black box problem. Inputs go in, predictions come out, and whatever happens in the middle remains obscure. For years, this was not a huge deal. A bad movie recommendation on Netflix is just a minor annoyance. But now, we handed those same mechanics over to lending, hiring, healthcare, and criminal justice. When a black box make a mistake in those areas, it can quietly upend a life.
WHY HIGH ACCURACY ISN’T ENOUGH
You will often hear a pragmatic counterargument. If the model works, why care how it got there? After all, medicine relies on treatments where precise cellular mechanisms are not fully mapped out. Yet we still trust the prescriptions.
That comparison falls apart pretty quickly. Medicine operates within a massive system of clinical trials, regulatory boards, and peer reviews designed to catch failures. Algorithmic decision making has almost none of that infrastructure. When a model quietly rejects a loan applicant or marks a job candidate as high risk, there is rarely an independent audit to check if that specific call was fair.
Accuracy alone can be deceiving. A hiring tool can be 95 percent accurate at predicting who a company will hire, while simply reflecting decades of biased hiring practices. Without a way to look inside, those systemic flaws stay neatly hidden behind a wall of high performance metrics.
THE PROMISE AND FLAWS OF EXPLAINABLE AI
This is where Explainable AI comes in. It is not a single silver bullet. It is a collection of techniques aimed at answering one question: why did the system make this specific choice?
Some developers opt for simpler, inherently interpretable models, like decision trees you can follow step by step, even if it means sacrificing a bit of predictive power. Others keep complex neural networks but use secondary tools to probe them, tweaking inputs slightly to see what causes outputs to change.
Yet, this field has its own blind spots:
Approximations are not truths. An explanation generated after a decision is made is not necessarily how the network processed data. It is often just a plausible story the tool tells about the outcome.
False confidence. A clean, easy to read chart can give teams a false sense of security, masking deeper statistical noise underneath.
Still, imperfect visibility is better than total darkness. If a model consistently flags an applicant zip code as a primary reason for denying credit, that is a red flag worth investigating, even if the explanation tool only captures part of the picture.
“If a decision heavily impacts your life, you deserve to know the reason behind it.”
WHAT IS ACTUALLY AT STAKE
This is not just an abstract debate for software engineers. The real cost falls on people receiving these decisions:
The job applicant filtered out by software without ever knowing why.
The patient assigned a low priority score by a triage tool that no nurse can override or explain.
The parent trying to appeal an automated school placement decision, only to be told that is just what the system generated.
At its heart, this is about basic accountability. Long before machine learning existed, we agreed on a simple principle. If a decision heavily impacts your life, you deserve to know the reason behind it, and you deserve a chance to challenge it if it is wrong.
WHERE WE GO FROM HERE
The solution is not to ditch advanced AI and revert entirely to basic spreadsheets. In high stakes fields, sacrificing predictive performance just for simplicity can lead to its own set of poor outcomes.
Instead, we need to stop treating accuracy as the only metric that matters. Explainability is not about making every line of code human readable. It is about ensuring that when a system impacts someone life, there is a clear path to answer the question that bank couldn’t: why did the machine make this choice?
As these tools take on heavier responsibilities, we have to evaluate them not just on how well they perform, but on how transparently they can account for their mistakes.
