  Operating safely and reliably despite continual distribution shifts is vital
for high-stakes machine learning applications. This paper builds upon the
transformative concept of ``antifragility'' introduced by (Taleb, 2014) as a
constructive design paradigm to not just withstand but benefit from volatility.
We formally define antifragility in the context of online decision making as
dynamic regret's strictly concave response to environmental variability,
revealing limitations of current approaches focused on resisting rather than
benefiting from nonstationarity. Our contribution lies in proposing potential
computational pathways for engineering antifragility, grounding the concept in
online learning theory and drawing connections to recent advancements in areas
such as meta-learning, safe exploration, continual learning,
multi-objective/quality-diversity optimization, and foundation models. By
identifying promising mechanisms and future research directions, we aim to put
antifragility on a rigorous theoretical foundation in machine learning. We
further emphasize the need for clear guidelines, risk assessment frameworks,
and interdisciplinary collaboration to ensure responsible application.
