  Machine learning (ML), being now widely accessible to the research community
at large, has fostered a proliferation of new and striking applications of
these emergent mathematical techniques across a wide range of disciplines. In
this paper, we will focus on a particular case study: the field of
paleoanthropology, which seeks to understand the evolution of the human species
based on biological and cultural evidence. As we will show, the easy
availability of ML algorithms and lack of expertise on their proper use among
the anthropological research community has led to foundational misapplications
that have appeared throughout the literature. The resulting unreliable results
not only undermine efforts to legitimately incorporate ML into anthropological
research, but produce potentially faulty understandings about our human
evolutionary and behavioral past.
  The aim of this paper is to provide a brief introduction to some of the ways
in which ML has been applied within paleoanthropology; we also include a survey
of some basic ML algorithms for those who are not fully conversant with the
field, which remains under active development. We discuss a series of missteps,
errors, and violations of correct protocols of ML methods that appear
disconcertingly often within the accumulating body of anthropological
literature. These mistakes include use of outdated algorithms and practices;
inappropriate train/test splits, sample composition, and textual explanations;
as well as an absence of transparency due to the lack of data/code sharing, and
the subsequent limitations imposed on independent replication. We assert that
expanding samples, sharing data and code, re-evaluating approaches to peer
review, and, most importantly, developing interdisciplinary teams that include
experts in ML are all necessary for progress in future research incorporating
ML within anthropology.
