 Home Technology AI How to Overcome the Model Bias in AI? # How to Overcome the Model Bias in AI?

3472 Mitigating model bias in the field of AI (artificial intelligence) is a challenging topic. It might sometimes mean bringing the calibration curves of the subgroups closer together. There is no standard solution for fixing model bias; and engineers are essentially asking how to make the model perform better for one or more subgroups. There are some standard techniques to be applied for improving model performance targeted towards subgroups and observe how they affect subgroup miscalibration.
When evaluating machine learning models for algorithmic bias, one of the main things the team looks at is the calibration curve. Calibration curves measure whether the model score accurately reflects the probability of the sample belonging to the positive class. When the calibration curve is compared across different subgroups, (e.g., plot the calibration curve separately for men and women), we are essentially asking whether the model is systematically over or underestimating the chance of the outcome occurring for some subgroups. The figure below shows a sample calibration curve. The x-axis shows the model score, while the y-axis shows the average number of samples labeled positive. The ideal curve should lie on the y=x line. For example, of all the samples assigned a score of 0.6 by the model, 60% of those should be labeled as positive. When the curve is above the y=x line, samples are being under-predicted; that is, the model score is less than the probability of the sample being labeled in the positive class. When the curve is below the y=x line, the model is over-predicting the probability. Calculating and plotting calibration curves is relatively easy. There are both external and internal libraries for doing such things. If the calibration curves across subgroups differ, the question then becomes how would one align the curves.
When a model is generally uncalibrated, the standard treatment is to apply either Platt scaling or isotonic regression to rescale the model output to reflect probabilities.
It stands to reason that simply applying these same techniques separately for each subgroup would be a straightforward way to ensure that the calibration curves are the same across subgroups. While it is okay to apply calibration to the overall model score, applying separate calibration to subgroups is not generally recommended, as it treats the symptom and not the underlying problem. Separate calibration also introduces procedural inconsistencies by subgroup, meaning we are using the subgroup label in making predictions. This may not be acceptable from a policy standpoint depending on the context. For instance, it may make sense to apply different score corrections by language, but if the subgroup were say, ideology, it may not be defensible to do so for conservative vs liberal content.
The use of calibration curves as a proxy to measure model performance across subgroups suggests that reducing disparities in calibration curves comes down to generally improving model performance, perhaps with an emphasis on the subgroup that is more miscalibrated. There is an implicit assumption that models are trained and evaluated on a different metric (e.g., precision at recall, FPR/FNR cost ratio), and that calibration provides another way to look at how the model is working. It is never correct to make the predictions worse for one group just to have equal calibrations.
Here are some things that might work: