Understanding Adversarial Training

Adversarial training refers to the process of preparing machine learning models to withstand intentional input perturbations that could mislead their predictions. This involves strategically manipulating inputs to identify scenarios where the model's outputs deviate from expected behavior, which is particularly vital in high-stakes fields like medicine and law. A practical example includes feeding biased or adversarial resumes to an AI screening tool to exploit its algorithms, showcasing the risk of models producing harmful or incorrect outputs. The broader implication is that these attacks can stem from knowledgeable adversaries looking to manipulate results, as well as unaware users whose inputs may inadvertently confuse the model.