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CHAPTER ONE Introduction Algorithms and artificial intelligence (AI) agents (or, jointly, artificial agents) influence many aspects of our lives: the news articles we read, the movies we watch, the people we spend time with, our access to credit, and even the investment of our capital. We have empowered them to make decisions and take actions on our behalf in these and many other domains because of the efficiency and speed gains they afford. Despite these benefits, we hear concerns about the rapid auto- mation of jobs (even cognitive jobs, such as journalism and radiology). This automation trend shows no signs of abating.1 As our reliance on artificial agents continues to grow, so does the risk. A better understanding of our attitudes and interactions with algorithms is essential precisely because of the aura of objectivity and infallibility our culture ascribes to algorithms (Bogost, 2015). What happens when we let what Goddard, Roudsari, and Wyatt (2012) called automation bias run rampant? Algorithmic errors in commercial recreational systems may have limited impact (e.g., sending someone on a bad date). But errant algorithms in infrastructure (electrical grids), defense systems, or financial markets could contain potentially high global security risk. The “Flash Crash” of 2010 illustrates how vulner- able our reliance on artificial agents can make us (Nuti et al., 2011). The White House’s Office of Science and Technology Policy has noted the threat our increasing reliance on opaque artificial agents presents 1 Autor (2010) highlighted a 12.6-percent decline in middle-skill automation-sensitive jobs from 1979–2009. Jaimovich and Siu (2012) found that these middle-class job losses acceler- ate in recessions and do not recover. 1PDF Image | Intelligence in Our Image Risks of Bias and Errors in Artificial Intelligence
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