  Innovations in AI have focused primarily on the questions of "what" and
"how"-algorithms for finding patterns in web searches, for instance-without
adequate attention to the possible harms (such as privacy, bias, or
manipulation) and without adequate consideration of the societal context in
which these systems operate. In part, this is driven by incentives and forces
in the tech industry, where a more product-driven focus tends to drown out
broader reflective concerns about potential harms and misframings. But this
focus on what and how is largely a reflection of the engineering and
mathematics-focused training in computer science, which emphasizes the building
of tools and development of computational concepts.
  As a result of this tight technical focus, and the rapid, worldwide explosion
in its use, AI has come with a storm of unanticipated socio-technical problems,
ranging from algorithms that act in racially or gender-biased ways, get caught
in feedback loops that perpetuate inequalities, or enable unprecedented
behavioral monitoring surveillance that challenges the fundamental values of
free, democratic societies.
  Given that AI is no longer solely the domain of technologists but rather of
society as a whole, we need tighter coupling of computer science and those
disciplines that study society and societal values.
