Abstract
Machine learning (ML) classification models are becoming
increasingly popular for tackling the sheer volume and speed of
online misinformation. In building these models data scientists
need to make assumptions about the legitimacy and
authoritativeness of the sources of ‘truth’ employed for model
training and testing. This has political, ethical and epistemic
implications which are rarely addressed in technical papers.
Despite (and due to) their reported high performance, ML-driven
moderation systems have the potential to shape public debate
and create downstream negative impacts. This article presents
findings from a responsible innovation (RI) inflected collaboration
between science and technology studies scholars and data
scientists. Following an interactive co-ethnographic process, we
identify a series of algorithmic contingencies—key moments
during ML model development which could lead to different
future outcomes, uncertainties and harmful effects. We conclude
by offering recommendations on how to address the potential
failures of ML tools for combating online misinformation.
