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25 - Human Perceptions of AI-Caused Harm

from Part III - Applications

Published online by Cambridge University Press:  17 May 2025

By
Gabriel Lima  and
Meeyoung Cha
Edited by
Kevin Tobia
Kevin Tobia
Affiliation:
Georgetown University, Washington DC
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Summary

The complexity involved in developing and deploying artificial intelligence (AI) systems in high-stakes scenarios may result in a “liability gap,” under which it becomes unclear who is responsible when things go awry. Scholarly and policy debates about the gap and its potential solutions have largely been theoretical, with little effort put into understanding the general public’s views on the subject. In this chapter, we present two empirical studies exploring laypeople’s perceptions of responsibility for AI-caused harm. First, we study the proposal to grant legal personhood to AI systems and show that it may conflict with laypeople’s policy preferences. Second, we investigate how people divide legal responsibility between users and developers of machines in a variety of situations and find that, while both are expected to pay legal damages, laypeople anticipate developers to bear the largest share of the liability in most cases. Our examples demonstrate how empirical research can help inform future AI regulation and provide novel lines of research to ensure that this transformative technology is regulated and deployed in a more democratic manner.

Keywords

AIartificial intelligenceAI systemalgorithmmachineliabilityresponsibilityempirical researchempirical studyliability gap

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Chapter
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The Cambridge Handbook of Experimental Jurisprudence , pp. 416 - 427
Publisher: Cambridge University Press
Print publication year: 2025

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