DHQ: Digital Humanities Quarterly
2025 19.1
Articles
Systematic bias in humanities datasets: ancient and
medieval coin finds in the FLAME projectLee Mordechai, Hebrew University of Jerusalem; Alan Stahl, Princeton University; Mark Pyzyk, Getty Research Institute; Ilia Curto Pelle, Princeton University
Abstract
[en]
FLAME is an online digital humanities project providing economic data for
investigations of the transition from Late Antiquity to the Early Middle Ages (CE
325-750) in Western Eurasia and North Africa. While accumulating, entering, and
displaying the data, the project’s leadership has become increasingly aware of the
inherent distortions in these data. These deviations operate on various levels, from
the disparate events that provide the coin finds that serve as the basis of its data
to previously unexamined scholarly biases that underpin such a quantitative approach
to historical analysis. By systematically examining these phenomena, we hope to frame
a discussion of such inherent biases in other digital humanities undertakings.
Unjust Readings: Against the New New CriticismPaul Barrett, University of Guelph
Abstract
[en]
Unjust Readings: Against the New New Criticism" offers a theoretical and methodological defence of the use of digital humanities methods for literary interpretation.
I argue that extant critiques of DH from Fish, Da, Eyers and others depend on an unexamined notion of the appropriate work of the humanities and, in particular,
of literary interpretation. Their claim, that critics should simply "just read" obscures the manner in which literary interpretation is never simply 'just reading.'
I argue that DH not only raises interpretive possibilities that would be impossible without digital tools but also foregrounds the methodological choices and theoretical
paradigms that so often are unstated, or implicit, in traditional humanities work. Inherent to the interpretive act that moves between the digital and the humanities is
a need to state how the critic works between the two, thereby making the interpretive frame explicit. I then demonstrate a number of examples, from my research and the work of others,
that demonstrate this productive capacity of DH in order to further refute that critics should simply 'just read' the texts.
Case Studies
Can Open-Source Fix Predictive Policing? Anti-Racist Critical Code Studies Approach
to Contemporary AI Policing SoftwareSarah Ciston, University of Southern California; Zach Mann, University of Southern California; Mark C. Marino, University of Southern California; Jeremy Douglass, University of California Santa Barbara
Abstract
[en]
Technology watchdogs and technoculture critics have discussed predictive policing software at an abstract level or have tried to reverse
engineer its blackboxed code. In this paper, we use the methods of Critical Code Studies, media archaeology, and software studies more
broadly to analyze CivicScape predictive policing software, published online, albeit partially. Working from an anti-racist approach, we
examine how the CivicScape code calculates which neighborhoods to recommend for heavy policing. Our reading demonstrates what code
analysis can add to the analysis of such software and makes a case for the public release of all legislative operational source code
for scrutiny under the principles of the Freedom of Information Act.
