Vulnerabilities And Ethical Implications In Machine Learning

Abstract

Recent times have witnessed a convergence of expansive datasets, cost-effective parallelized computational capabilities, and progress in statistical learning techniques, particularly deep learning. This convergence has significantly propelled the integration of machine learning (ML) into commonplace applications. Machine learning models have proven their utility across diverse contexts, spanning from visual recognition tasks to personalized recommendation systems and the analysis of human language. Despite their widespread employment, the exact nature of more complex models as well as the details of their decision-making processes elude the understanding of much of the technical community. Such systems contain nebulous vulnerabilities that need to be better understood and guarded against, especially in critical applications like autonomous vehicle navigation. Recent research has elucidated some of these threats against ML systems, known as "adversarial attacks," and has attempted to describe mechanisms for both attack and defense. Within this document, we elucidate ongoing investigations, showcase tangible instances of hostile interventions, juxtapose various approaches for crafting disruptive instances, and finally delve into the ethical ramifications stemming from these susceptibilities in ML frameworks. We conclude that certain defensive measures, namely adversarial training, should be employed when creating production ready ML models.