John Danaher and Sven Nyholm have argued that automation, especially of the sort powered by artificial intelligence, poses a threat to meaningful work by diminishing the chances for meaning-conferring workplace achievement, what they call “achievement gaps”. In this paper, I argue that Danaher and Nyholm’s achievement gap thesis suffers from an ambiguity. The weak version of the thesis holds that automation may result in the appearance of achievement gaps, whereas the strong version holds that automation may result on balance loss (...) in possibilities for workplace achievements, i.e., in the appearance of an overall gappier work landscape. Against the strong version of the achievement gap thesis, I characterize situations where automation may result in boons to meaning-conferring workplace achievements: the appearance of what I call achievement spread and achievement swaps. Nevertheless, Danaher and Nyholm are right to worry about some uses of automation whereby human workers become subservient to AI. But these situations are better framed, I argue, as autonomy gaps rather than achievement gaps. (shrink)

Imagining AI: How the World Sees Intelligent Machines edited by Stephen Cave and Kanta Dihal (2023) was published as a result of the Global AI Narratives (GAIN) project of the Leverhulme Center for the Future of Intelligence, University of Cambridge. The book has 25 chapters grouped into four parts, corresponding to different geographical regions: Europe; the Americas and Pacific; Africa, Middle East, and South Asia; East and South East Asia. All chapters contributed comprehensively to the mission of showing “the imaginary (...) of intelligent machines is an incredibly rich site for exploring” and how the imaginaries shape the ways “different cultures reconcile the demands of being human in a technological age.”. (shrink)