  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.
