  There is an increasing interest in ensuring machine learning (ML) frameworks
behave in a socially responsible manner and are deemed trustworthy. Although
considerable progress has been made in the field of Trustworthy ML (TwML) in
the recent past, much of the current characterization of this progress is
qualitative. Consequently, decisions about how to address issues of
trustworthiness and future research goals are often left to the interested
researcher. In this paper, we present the first quantitative approach to
characterize the comprehension of TwML research. We build a co-occurrence
network of words using a web-scraped corpus of more than 7,000 peer-reviewed
recent ML papers -- consisting of papers both related and unrelated to TwML. We
use community detection to obtain semantic clusters of words in this network
that can infer relative positions of TwML topics. We propose an innovative
fingerprinting algorithm to obtain probabilistic similarity scores for
individual words, then combine them to give a paper-level relevance score. The
outcomes of our analysis inform a number of interesting insights on advancing
the field of TwML research.
