  How has recent AI Ethics literature addressed topics such as fairness and
justice in the context of continued social and structural power asymmetries? We
trace both the historical roots and current landmark work that have been
shaping the field and categorize these works under three broad umbrellas: (i)
those grounded in Western canonical philosophy, (ii) mathematical and
statistical methods, and (iii) those emerging from critical
data/algorithm/information studies. We also survey the field and explore
emerging trends by examining the rapidly growing body of literature that falls
under the broad umbrella of AI Ethics. To that end, we read and annotated
peer-reviewed papers published over the past four years in two premier
conferences: FAccT and AIES. We organize the literature based on an annotation
scheme we developed according to three main dimensions: whether the paper deals
with concrete applications, use-cases, and/or people's lived experience; to
what extent it addresses harmed, threatened, or otherwise marginalized groups;
and if so, whether it explicitly names such groups. We note that although the
goals of the majority of FAccT and AIES papers were often commendable, their
consideration of the negative impacts of AI on traditionally marginalized
groups remained shallow. Taken together, our conceptual analysis and the data
from annotated papers indicate that the field would benefit from an increased
focus on ethical analysis grounded in concrete use-cases, people's experiences,
and applications as well as from approaches that are sensitive to structural
and historical power asymmetries.
