Trustworthy Machine Learning under Social and Adversarial Data Sources
This work addresses the problem of ensuring reliable machine learning performance for systems interacting with strategic or malicious data sources, which is incremental as it builds on existing concerns about adversarial examples and strategic behavior.
The paper tackles the problem of machine learning systems being affected by social and adversarial behaviors from data sources, which can degrade performance, such as making deep neural networks susceptible to adversarial examples. It aims to address these challenges to ensure trustworthy machine learning in societal settings.
Machine learning has witnessed remarkable breakthroughs in recent years. As machine learning permeates various aspects of daily life, individuals and organizations increasingly interact with these systems, exhibiting a wide range of social and adversarial behaviors. These behaviors may have a notable impact on the behavior and performance of machine learning systems. Specifically, during these interactions, data may be generated by strategic individuals, collected by self-interested data collectors, possibly poisoned by adversarial attackers, and used to create predictors, models, and policies satisfying multiple objectives. As a result, the machine learning systems' outputs might degrade, such as the susceptibility of deep neural networks to adversarial examples (Shafahi et al., 2018; Szegedy et al., 2013) and the diminished performance of classic algorithms in the presence of strategic individuals (Ahmadi et al., 2021). Addressing these challenges is imperative for the success of machine learning in societal settings.