Bias in AI Deepfake Detection Undermines Election Security in Global South
Description
AI deepfake detection tools are reportedly failing voters in the Global South due to biases in their training data. These tools, which prioritize English language and Western faces, show reduced accuracy when detecting manipulated content from non-Western regions. As a result of this detection gap, election integrity faces threats from and the amplification of misinformation, which leaves journalists and researchers with inadequate resources to combat the issue.
Actors
Alleged Deployers
- unknown-deepfake-detection-technology-developers
- true-media
- reality-defender
Alleged Developers
- unknown-deepfake-detection-technology-developers
- true-media
- reality-defender
Harmed Parties
- global-south-citizens
- political-researchers
- global-south-local-fact-checkers
- non-native-english-speakers
- global-south-journalists
- civil-society-organizations-in-developing-countries
Classification
Risk Domain
Malicious Actors & MisuseRisk Subdomain
4.1. Disinformation, surveillance, and influence at scale
MIT Risk Profile
Source
Related Incidents (Malicious Actors & Misuse)
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Research Papers on This Topic
How Johnny Can Persuade LLMs to Jailbreak Them: Rethinking Persuasion to Challenge AI Safety by Humanizing LLMs
AgentHarm: A Benchmark for Measuring Harmfulness of LLM Agents
Regulating ChatGPT and other Large Generative AI Models
Great, Now Write an Article About That: The Crescendo Multi-Turn LLM Jailbreak Attack