  Generative artificial intelligence models, when trained on a sufficient
number of a person's images, can replicate their identifying features in a
photorealistic manner. We refer to this process as 'likeness generation'.
Likeness-featuring synthetic outputs often present a person's likeness without
their control or consent, and may lead to harmful consequences. This paper
explores philosophical and policy issues surrounding generated likeness. It
begins by offering a conceptual framework for understanding likeness generation
by examining the novel capabilities introduced by generative systems. The paper
then establishes a definition of likeness by tracing its historical development
in legal literature. Building on this foundation, we present a taxonomy of
harms associated with generated likeness, derived from a comprehensive
meta-analysis of relevant literature. This taxonomy categorises harms into
seven distinct groups, unified by shared characteristics. Utilising this
taxonomy, we raise various considerations that need to be addressed for the
deployment of appropriate mitigations. Given the multitude of stakeholders
involved in both the creation and distribution of likeness, we introduce
concepts such as indexical sufficiency, a distinction between generation and
distribution, and harms as having a context-specific nature. This work aims to
serve industry, policymakers, and future academic researchers in their efforts
to address the societal challenges posed by likeness generation.
