2024 study in the American Political Science Review

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Cambridge University Press(peer-reviewed)·cambridge.org/core/journals/american-political-science-re...

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5/5

Gold(5)

Gold standard. Rigorous peer review, high editorial standards, and strong institutional reputation.

Rating inherited from publication venue: Cambridge University Press

Relevant to AI safety discussions on synthetic media governance and the societal risks of generative AI, particularly how deepfake awareness can paradoxically be exploited to undermine trust in authentic evidence.

Metadata

Importance: 62/100journal articleprimary source

Summary

This 2024 APSR study examines how the widespread awareness of deepfakes and misinformation enables politicians to falsely deny authentic evidence of their misconduct by claiming it is fabricated. The research demonstrates that this 'liar's dividend' undermines democratic accountability, as voters become uncertain whether real evidence is genuine or AI-generated.

Key Points

•Politicians can exploit public awareness of deepfakes to dismiss authentic incriminating evidence as AI-generated misinformation, evading accountability.
•The 'liar's dividend' effect may be as damaging as misinformation itself, eroding the epistemic foundations of democratic accountability.
•Experimental evidence suggests voters are susceptible to false denials of authentic media, especially in politically polarized contexts.
•The phenomenon highlights a dual threat: not just fake content misleading people, but real content being dismissed as fake.
•Findings have implications for AI governance and policy around synthetic media disclosure and authentication standards.

Cited by 2 pages

Page	Type	Quality
Epistemic Collapse	Risk	49.0
AI-Driven Trust Decline	Risk	55.0

Cached Content Preview

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The Liar’s Dividend: Can Politicians Claim Misinformation to Evade Accountability? | American Political Science Review | Cambridge Core 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

 
 
 
 
 

 
 
 

 

 
 
 
 
 
 
 
 

 

 
 

 

 

 
 
 
 
 
 
 
 

 

 
 

 
 

 
 

 

 

 

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Resource ID: c75d8df0bbf5a94d | Stable ID: sid_zrmlwmm9ZI