It is known that adversarial examples can exploit deep learning models’ vulnerabilities to cause harm or gain unfair advantages. Recently, many works have tried to improve adversarial examples to re-train models and make them more robust. However, we have shown in our prior work that those mechanisms negatively affect different measures of fairness, which are critical in practice. This talk will show how adversarial training decreases fairness scores and how we can make assessments and estimations of such behavior. Furthermore, we show how to evaluate adversarial robustness in classic and generative models using computer vision datasets. These estimations can help researchers define creative objective functions for safe, robust, trustworthy models.