Bias in AI Deepfake Detection Undermines Election Security in Global South

September 2, 2024

AI deepfake detection tools, predominantly trained on English and Western data, are reportedly falling short when detecting manipulated content from non-Western regions. This bias poses a significant challenge to election integrity in the Global South as it allows for misinformation amplification with limited resources available to combat it. For those interested in shaping safe and secure AI practices, explore HISPI Project Cerebellum TAIM (Govern) and contribute to our AI incident database for harm prevention.

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Matched TAIM controls

Suggested mapping from embedding similarity (not a formal assessment). Browse all TAIM controls

MEASURE 2.10 — similarity 0.675, rank 1. TAIM detail and related incidents →
MAP 1.6 — similarity 0.659, rank 2. TAIM detail and related incidents →
MAP 4.1 — similarity 0.652, rank 3. TAIM detail and related incidents →

Alleged deployer: unknown-deepfake-detection-technology-developers, true-media, reality-defender
Alleged developer: unknown-deepfake-detection-technology-developers, true-media, reality-defender
Alleged 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

Source

Data from the AI Incident Database (AIID). Cite this incident: https://incidentdatabase.ai/cite/801

Data source

Incident data is from the AI Incident Database (AIID).

When citing the database as a whole, please use:

McGregor, S. (2021) Preventing Repeated Real World AI Failures by Cataloging Incidents: The AI Incident Database. In Proceedings of the Thirty-Third Annual Conference on Innovative Applications of Artificial Intelligence (IAAI-21). Virtual Conference.

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