Detecting bias in recruitment AI requires moving beyond gut feelings toward measurable statistical tests. Most engineers start with fairness metrics like disparate impact ratios. This involves checking if the selection rate for a protected group is less than 80% of the rate for the highest-performing group. If the numbers don't line up, your model likely favors certain demographics.
You might also use tools like IBM's AI Fairness 360 or Google's What-If Tool. These frameworks help you run counterfactual tests. For instance, if you change a candidate's gender in the data but keep everything else the same, does the bot change its decision? If it does, you have a direct bias problem. However, relying solely on these tools is risky. A metric might show fairness, yet the model could still use
