Merging AI Incidents Research with Political Misinformation Research: Introducing the Political Deepfakes Incidents Database

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Authors: Christina P. Walker, Daniel S. Schiff, Kaylyn Jackson Schiff

Published: 2024-09-05 19:24:38+00:00

AI Summary

This paper introduces the Political Deepfakes Incidents Database (PDID), a curated collection of politically relevant deepfakes (videos, images, and cheapfakes) with metadata and researcher-coded descriptors. The PDID aims to facilitate research on the prevalence, trends, and impact of political deepfakes, benefiting researchers, policymakers, and the public.

Abstract

This article presents the Political Deepfakes Incidents Database (PDID), a collection of politically-salient deepfakes, encompassing synthetically-created videos, images, and less-sophisticated `cheapfakes.' The project is driven by the rise of generative AI in politics, ongoing policy efforts to address harms, and the need to connect AI incidents and political communication research. The database contains political deepfake content, metadata, and researcher-coded descriptors drawn from political science, public policy, communication, and misinformation studies. It aims to help reveal the prevalence, trends, and impact of political deepfakes, such as those featuring major political figures or events. The PDID can benefit policymakers, researchers, journalists, fact-checkers, and the public by providing insights into deepfake usage, aiding in regulation, enabling in-depth analyses, supporting fact-checking and trust-building efforts, and raising awareness of political deepfakes. It is suitable for research and application on media effects, political discourse, AI ethics, technology governance, media literacy, and countermeasures.

Key findings

The PDID provides a valuable resource for analyzing the prevalence and impact of political deepfakes. It enables research into various aspects, including communication goals, harm types depicted, real-world connections, and policy implications. The database facilitates empirical studies to understand the causal links between deepfakes and real-world outcomes.

Approach

The authors created the PDID database by manually collecting data from English-language social media and news websites, using a snowball sampling technique. They developed a codebook to guide data collection and coding, drawing on existing taxonomies from AI incident databases and incorporating variables relevant to political science and communication research.

Datasets

Political Deepfakes Incidents Database (PDID), which includes deepfakes dating back to 2017; data primarily from English language social media posts and popular news websites.

Model(s)

UNKNOWN

Author countries

USA

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