# Detecting and Processing Unuspected Sensitive Variables for Robust Machine Learning
## 6 A Generic Pipeline to Detect and to Treat Unsuspected Sensitive Variables
In phase \(B\), i.e., between epochs 12 and 18, the training algorithm has converged when observed on all training images but not yet on blue-veil images. This is due to the fact that the blue-veil images only represent a small fraction of the training set. Note that if only measuring the convergence on the whole training set, it would be tempting to stop the training process at the beginning of phase \(B\), which would obviously lead to a different treatment of the blue-veiled images and other images (see \(\delta\) loss 1 in Figure 5). More interestingly for us, the convergence curves are stable on the training set in phase \(C\), but it regularly decreases on the test set. At the end of phase \(C\), the convergence curve is stable on the whole test set, and it is common practice to stop the training process there (early stopping principle). However, itis important to remark that the generalisation properties of the trained neural network are still much poorer for blue-veiled images than other images (see \(\delta\) loss 2 in Figure 5). This actually explains in Figure 4a the different accuracies observed for the blue-veiled images with respect to the other images. We indeed stopped the convergence after 50 epochs there. Although noisy, the convergence curve obtained on blue-veil test images slowly decreases in phase \(D\) until reaching an optimal value at epoch 145 (see \(\delta\) loss 3 in Figure 5). Finally, the training algorithm starts over-fitting the blue-veiled images in phase \(E\), so the training process should be stopped at its very beginning. It is then essential to point out that obtaining reasonably good generalisation properties on blue-veiled images required about 3 times more epochs than what is made using what is commonly considered to be the good practices, and about 12 times more epochs than what would be made using a naive approach. ## 8 Conclusions

We have addressed in this paper the issue of algorithmic bias detection and mitigation in machine learning, with a particular focus on complex and high-dimensional data. While, in societal applications, the common sense or new regulations can help the data scientists detecting the sensitive variables related to algorithmic biases, this task can be much more complex in industrial application. The sensitive variables can indeed be unsuspected in this case, as illustrated in our remote sensing use-case, where some training images presented a reconstruction artefact (the blue-veil effect) making the predictions inaccurate. After stating the various causes of algorithmic bias in machine learning and reviewing the main strategies for measuring, detecting and correcting bias, we then developed in Section 6 a new pipeline to deal with the problem of unknown bias on unsuspected sensitive variables. Interestingly, the application of our pipeline to the use-case of Section 7 additionally pushed us to understand what was the source of the bias on the blue-veil images. In this case, this was due to an insufficient algorithm convergence on this subgroup of images, probably because they were particularly different to other images. We then reframed the problem as an optimizing problem, where we carefully monitored the learning process in order to learn without creating algorithmic bias. This improved learning procedure was, however, only made possible because the unsuspected sensitive variable was identified using our Human-in-the-loop strategy. To go further than our study, we also believe that an in-depth work is necessary before deploying an AI-based solution, in particular when it will be used for sensitive applications. In that sense, we should see the different regulations arising as an opportunity to gain knowledge on data, deep learning, and optimisation instead of a brake on innovation. Figure 5: Detailed convergence of the BCE-loss on all data and on blue-veil images only. Results obtained on the training set (**left**) and on the test set (**right**) are represented.