The Turing Test—a thought experiment in which a human and a computer both try to convince an interrogator that they are human—is actually based on another thought experiment by Alan Turing, the Imitation Game—in which a man and woman both try to convince an interrogator that they are a woman. Why is it significant that a theoretical test by which we measure human-like performance was first inspired by an experiment in gendered performance? Today, machines that we increasingly rely on for decision making, including machine learning AI, are described as demonstrating high IQ. Meanwhile, AI systems designed to provide EQ-heavy labour in care, customer service, and comfort are predominantly female presenting. In this talk, Fan will explore how the gendering of AI assistants is just a new method in a long history of abstracting women and their bodies into labouring machines. She argues that AI assistants play a modified Imitation Game, trying to trick users into accepting machine as woman. By exploring industry designers’ research findings, Fan will argue that when testing such AI for their human-like performance, designers are not looking for intelligence, but rather, for efficacy to get menial labour done—with a smile.

Los investigadores deben citar este trabajo de la siguiente manera:

• Fan, L., (2023), "Playing an Imitation Game with Apple’s Siri: E.Q, I.Q., and the Gendered Design of Artificial and Automated Intelligence", HSSCommons: (DOI: 10.25547/2Y73-ND26)

  Copy Citation | Export metadata as... | JSON | CSV | RO-Crate | BibTex | EndNote

This talk was delivered virtually on June 15, 2023, as an Institute Lecture as part of the Digital Humanities Summer Institute (University of Victoria, British Columbia, Canada). The session was chaired by Aaron Mauro (Brock U).