The Fairness Metrics Your ML Model Needs -And Why Accuracy Isn't One of Them

<h1> Introduction¶ </h1> <p>Artificial intelligence now shapes access to credit, housing, employment, healthcare, and justice. In each of these domains, the decisions an algorithm

<p>Your model has 95% accuracy. You ship it.</p> <p>Three weeks later someone tells you it's missing 40% of actual fraud cases.</p> <p>You check. The dataset had 95% legit

<p>Last post you saw that accuracy can be 95% while your model catches zero fraud.</p> <p>Precision and recall are the fix. They measure different things, they pull in opposite

<p>Building a machine learning model is relatively straightforward today.</p> <p>You train it.<br /> Evaluate it.<br /> Tune it.</p> <p>Eventually, you get a model that performs

<p><strong>The moment a metric becomes the target, it stops being a good metric</strong></p> <p>When you say "improve accuracy," that "accuracy" isn't a law of physics. It's

<!-- SC_OFF --><div class="md"><p><strong>Submitted by:</strong> Adam Kruger <strong>Date:</strong> March 23, 2026 <strong>Models Solved:</strong> 3/3 (M1, M2, M3) + Warmup</p>

<p>A while back, I had to retract one of my own results.</p> <p>A model I'd built was posting a validation score I was genuinely proud of. Then I looked closer: the test set