of the nation’s nuclear defense system as part of President Reagan’s “Star Wars” Strategic
Defense Initiative. Thankfully, a group called the Computer Professionals for Social
Responsibility convinced DARPA any system of that size was bound to contain bugs, and
therefore to pursue it would be tantamount to creating a “doomsday machine” (Roland
& Shiman 2002). Luckily, it was called off. In this case, knowledge of one kind of AI risk
was critical to offset the creation of another, greater risk.
This paper asks, how have the risks of artificial intelligence (AI) been framed,
represented, and understood in America since the field’s “official” founding at a small
conference at Dartmouth College in 1956? Due in part to their speculative nature, the
risks of AI have been understudied. The discipline of AI itself has historically paid scant
attention to ‘social impacts’ and other non-technical subjects such as risk. As
philosopher and longtime critic of AI Hubert Dreyfus argued in What Computers Can’t
Do (1972), “artificial intelligence is the least self-critical field on the scientific scene.”
In reaction to technical experts’ organized ignorance, the first four decades of AI
saw a number of American critics of AI rise to prominence, including Mumford, Joseph
Weizenbaum, Hubert Dreyfus, Terry Winograd, John Searle, and so on. These and other
scholars constitute a “minor literature” within the broader constellation of AI discourse,
yet the common threads connecting their contributions remain largely unmapped.
Focusing on what I call the “classic period” of AI (1956-1996), this paper examines this
lineage of prominent American AI critics, looking for categorizations and constructions
of AI risks, and considerations of those at-risk. However, this paper looks beyond these
“usual suspects” to include the relevant writings of US-based social movements, such as
Science for the People and Computer Professionals for Responsibility, that were actively
involved in challenging dominant forms of technoscience.
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