Use and Misuse of Machine Learning in Anthropology

Jeff Calder, Reed Coil, J. Anne Melton, Peter Olver, Gilbert Tostevin, Katrina Yezzi-Woodley

Research output: Contribution to journalArticlepeer-review

Abstract

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 (e.g. bones, genetics) and cultural (e.g. stone
tools) 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 These mistakes include use of outdated algorithms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 testing/training 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
and beyond.
Original languageEnglish
JournalIEEE Bits
Publication statusPublished - 2022

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