  Research in Fairness, Accountability, Transparency, and Ethics (FATE) has
established many sources and forms of algorithmic harm, in domains as diverse
as health care, finance, policing, and recommendations. Much work remains to be
done to mitigate the serious harms of these systems, particularly those
disproportionately affecting marginalized communities. Despite these ongoing
harms, new systems are being developed and deployed which threaten the
perpetuation of the same harms and the creation of novel ones. In response, the
FATE community has emphasized the importance of anticipating harms. Our work
focuses on the anticipation of harms from increasingly agentic systems. Rather
than providing a definition of agency as a binary property, we identify 4 key
characteristics which, particularly in combination, tend to increase the agency
of a given algorithmic system: underspecification, directness of impact,
goal-directedness, and long-term planning. We also discuss important harms
which arise from increasing agency -- notably, these include systemic and/or
long-range impacts, often on marginalized stakeholders. We emphasize that
recognizing agency of algorithmic systems does not absolve or shift the human
responsibility for algorithmic harms. Rather, we use the term agency to
highlight the increasingly evident fact that ML systems are not fully under
human control. Our work explores increasingly agentic algorithmic systems in
three parts. First, we explain the notion of an increase in agency for
algorithmic systems in the context of diverse perspectives on agency across
disciplines. Second, we argue for the need to anticipate harms from
increasingly agentic systems. Third, we discuss important harms from
increasingly agentic systems and ways forward for addressing them. We conclude
by reflecting on implications of our work for anticipating algorithmic harms
from emerging systems.
