1,721,153 research outputs found
Resource-Constrained Low-Power Bus Encoding with Crosstalk Delay Elimination
No full textThis work was supported by the Korea Sci-
ence and Engineering Foundation (KOSEF) through the Advanced
Information Technology Research Center (AITrc)
Tracking Economic Disparities in North Korea: Satellite Imagery Analysis Using AI with Policy Insight
No full textBlaming Humans and Machines: What Shapes People's Reactions to Algorithmic Harm
Full text linkArtificial intelligence (AI) systems can cause harm to people. This research
examines how individuals react to such harm through the lens of blame. Building
upon research suggesting that people blame AI systems, we investigated how
several factors influence people's reactive attitudes towards machines,
designers, and users. The results of three studies (N = 1,153) indicate
differences in how blame is attributed to these actors. Whether AI systems were
explainable did not impact blame directed at them, their developers, and their
users. Considerations about fairness and harmfulness increased blame towards
designers and users but had little to no effect on judgments of AI systems.
Instead, what determined people's reactive attitudes towards machines was
whether people thought blaming them would be a suitable response to algorithmic
harm. We discuss implications, such as how future decisions about including AI
systems in the social and moral spheres will shape laypeople's reactions to
AI-caused harm.Comment: ACM CHI 202
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