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How deepfakes undermine truth and threaten democracy

2019
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In brief

Deepfakes use machine-learning technology to create fabricated audio and video of people doing or saying things they never did. Law professor Danielle Citron explores the dual threats: individual harm (non-consensual sexual deepfakes targeting women, especially minorities and journalists) and societal damage (eroding trust in institutions, enabling the "liar's dividend" where bad actors dismiss real evidence as fake). She calls for coordinated responses from tech platforms, lawmakers, law enforcement, and public media literacy.

Summary figure for How deepfakes undermine truth and threaten democracy

TEDSummit 2019 — Watch on YouTube

Key themes

Individual harms: Citron opens with the case of Rana Ayyub, an Indian journalist whose non-consensual sexual deepfake went viral in 2018, resulting in doxing, harassment, threats, and lasting psychological trauma. The gendered and racialized nature of deepfake abuse is clear: women and minority journalists face disproportionate targeting.

Why deepfakes are weapons: Three factors combine to make deepfakes particularly dangerous:

  1. Visceral belief in video/audio — humans have an intuitive, deep trust in what they see and hear, even when that trust is misplaced.
  2. Negative information spreads faster — research shows online hoaxes spread 10× faster than accurate stories; humans are drawn to salacious, provocative content.
  3. Confirmation bias amplified by social media — platforms let us instantly and widely share information that aligns with our existing viewpoints.

Societal threats: Deepfakes could undermine democratic legitimacy (showing a candidate gravely ill the night before an election), economic stability (showing a bank CEO drunk before an IPO), and international relations (showing soldiers burning religious texts, then another deepfake showing an imam praising the attack—potentially sparking global violence).

The "liar's dividend": Bad actors already invoke deepfakes to dismiss real evidence of wrongdoing (claiming audio recordings of their statements are fake). As deepfake detection becomes harder, the problem will worsen: not only will we believe false content, we'll start disbelieving truth itself.

Policy and response

Citron outlines a multi-pronged approach:

  • Tech platforms: Change terms of service to ban harmful deepfakes, while protecting satire, art, and education. Requires human judgment and is expensive.
  • Law: Most jurisdictions lack tailored legislation on digital impersonations and invasion of sexual privacy. Citron and Dr. Mary Anne Franks are working with US lawmakers; similar efforts are underway in Iceland, UK, and Australia.
  • Law enforcement training: Police currently lack education about deepfakes and the relevant laws. Citron's research on cyberstalking found law enforcement dismissed online abuse as trivial.
  • Media and public literacy: Journalists need training to avoid amplifying deepfakes. Citizens need to develop "far better radar for fakery" before clicking, sharing, and liking.

Notes

A foundational public talk on deepfakes. Citron's framing centers the gendered and racialized dimensions of deepfake abuse often overlooked in purely technical discussions. The "liar's dividend" concept is particularly valuable: it captures not just the direct harm of false deepfakes but the secondary threat of eroding epistemic trust. The talk makes clear that technical detection alone is insufficient — the problem is social, legal, and political as much as technological.

How it connects

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Summarized from the full text. Added 2026-11-09 from a submission by reza. Every statement on this page traces back to the source.