Recent events have highlighted large-scale systemic racial disparities in U.S. criminal justice based on race and other demographic characteristics. Although criminological datasets are used to study and document the extent of such disparities, they often lack key information, including arrestees’ racial identification. As AI technologies are increasingly used by criminal justice agencies to make predictions about outcomes in bail, policing, and other decision-making, a growing literature suggests that the current implementation of these systems may perpetuate racial inequalities. In this paper, (...) we argue that AI technologies should be investigated to understand how they recognize racial categories and whether they can be harnessed to fill missing race data. By bridging this gap, we can work towards better understanding racial inequalities in a wide range of contexts, most notably criminal justice. Using a multidisciplinary perspective, we rethink the design and methodology used in facial processing technology (FPT) based on supervised deep learning model (DLM) image classification. By modifying standard FPT pipelines to tackle multiple sources of DLM bias, we propose an experimental methodology based on ethical AI principles to generate binary (Black and White) racial categories using mugshots. We go beyond simply reporting DLM accuracies and address fundamental issues such as generalizability and interpretability by using a “self-auditing” approach. First, we evaluate the inference performances of 42 fine-tuned DLMs using unseen test images from the same dataset but subject to varying data augmentations. Next, to interpret and validate our methodological approach, we apply gradient-based saliency maps to assess the consistency of facial region relevance and attribution. Finally, drawing upon insights from three areas (computer science, sociology, and law), we investigate the efficacy of our DLM-based method as a tool for detecting racial inequalities in criminal justice. (shrink)

We are all used to drawing straight lines to represent time, and above them, we plot historical events or physical or economic data. What to us is a self-evident convention, is however of an astonishingly recent date: it emerged only in the second half of the eighteenth century. To us, this late date seems paradoxical and cries out for an explanation. How else did earlier periods measure change, if not as a function of time? it will be argued that since (...) Antiquity, time was taken to measure change, and change to occur in space. ‘Our’ idea of representing time as an independent dimension would have seemed aberrant. But then, a second issue arises. Did not medieval natural philosophers employ timelines, Oresme’s diagram of the mean speed theorem being the most famous case? However, as will be shown, our interpretation of his diagram is probably wrong. This insight, in turn, takes care of a third paradox, namely Galileo’s initial inability to represent the law of free fall correctly. This article will document that the timeline first emerged in the late sixteenth century in works on chronology, made its first appearance in physics in Galileo’s diagrams, and had its general breakthrough in the eighteenth century. (shrink)