  Machine learning (ML) and artificial intelligence (AI) tools increasingly
permeate every possible social, political, and economic sphere; sorting,
taxonomizing and predicting complex human behaviour and social phenomena.
However, from fallacious and naive groundings regarding complex adaptive
systems to datasets underlying models, these systems are beset by problems,
challenges, and limitations. They remain opaque and unreliable, and fail to
consider societal and structural oppressive systems, disproportionately
negatively impacting those at the margins of society while benefiting the most
powerful.
  The various challenges, problems and pitfalls of these systems are a hot
topic of research in various areas, such as critical data/algorithm studies,
science and technology studies (STS), embodied and enactive cognitive science,
complexity science, Afro-feminism, and the broadly construed emerging field of
Fairness, Accountability, and Transparency (FAccT). Yet, these fields of
enquiry often proceed in silos. This thesis weaves together seemingly disparate
fields of enquiry to examine core scientific and ethical challenges, pitfalls,
and problems of AI.
  In this thesis I, a) review the historical and cultural ecology from which AI
research emerges, b) examine the shaky scientific grounds of machine prediction
of complex behaviour illustrating how predicting complex behaviour with
precision is impossible in principle, c) audit large scale datasets behind
current AI demonstrating how they embed societal historical and structural
injustices, d) study the seemingly neutral values of ML research and put
forward 67 prominent values underlying ML research, e) examine some of the
insidious and worrying applications of computer vision research, and f) put
forward a framework for approaching challenges, failures and problems
surrounding ML systems as well as alternative ways forward.
