  A key concern with the concept of "alignment" is the implicit question of
"alignment to what?". AI systems are increasingly used across the world, yet
safety alignment is often focused on homogeneous monolingual settings.
Additionally, preference training and safety measures often overfit to harms
common in Western-centric datasets. Here, we explore the viability of different
alignment approaches when balancing dual objectives: addressing and optimizing
for a non-homogeneous set of languages and cultural preferences while
minimizing both global and local harms. We collect the first set of human
annotated red-teaming prompts in different languages distinguishing between
global and local harm, which serve as a laboratory for understanding the
reliability of alignment techniques when faced with preference distributions
that are non-stationary across geographies and languages. While this setting is
seldom covered by the literature to date, which primarily centers on English
harm mitigation, it captures real-world interactions with AI systems around the
world. We establish a new precedent for state-of-the-art alignment techniques
across 6 languages with minimal degradation in general performance. Our work
provides important insights into cross-lingual transfer and novel optimization
approaches to safeguard AI systems designed to serve global populations.
