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

• URL: http://arxiv.org/abs/2409.15319v1
• Date: Thu, 5 Sep 2024 19:24:38 GMT
• Title: Merging AI Incidents Research with Political Misinformation Research: Introducing the Political Deepfakes Incidents Database
• Authors: Christina P. Walker, Daniel S. Schiff, Kaylyn Jackson Schiff,
• Abstract summary: 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Understanding Audiovisual Deepfake Detection: Techniques, Challenges, Human Factors and Perceptual Insights [49.81915942821647]
  Deep Learning has been successfully applied in diverse fields, and its impact on deepfake detection is no exception. Deepfakes are fake yet realistic synthetic content that can be used deceitfully for political impersonation, phishing, slandering, or spreading misinformation. This paper aims to improve the effectiveness of deepfake detection strategies and guide future research in cybersecurity and media integrity.
  arXiv  Detail & Related papers  (2024-11-12T09:02:11Z)
• Towards Hybrid Intelligence in Journalism: Findings and Lessons Learnt from a Collaborative Analysis of Greek Political Rhetoric by ChatGPT and Humans [1.4605550954028836]
  The chapter delves into various aspects of political discourse analysis, including sentiment analysis, polarization, populism, topic detection, and Named Entities Recognition (NER) The project stands as an innovative example of human-AI collaboration within the realm of digital humanities, offering valuable insights for future initiatives.
  arXiv  Detail & Related papers  (2024-10-17T09:54:54Z)
• Biased AI can Influence Political Decision-Making [64.9461133083473]
  This paper presents two experiments investigating the effects of partisan bias in AI language models on political decision-making. We found that participants exposed to politically biased models were significantly more likely to adopt opinions and make decisions aligning with the AI's bias.
  arXiv  Detail & Related papers  (2024-10-08T22:56:00Z)
• A Survey of Stance Detection on Social Media: New Directions and Perspectives [50.27382951812502]
  stance detection has emerged as a crucial subfield within affective computing. Recent years have seen a surge of research interest in developing effective stance detection methods. This paper provides a comprehensive survey of stance detection techniques on social media.
  arXiv  Detail & Related papers  (2024-09-24T03:06:25Z)
• Examining the Implications of Deepfakes for Election Integrity [9.129491613898962]
  It is becoming cheaper to launch disinformation operations at scale using AI-generated content, in particular 'deepfake' technology. We discuss the threats from deepfakes in politics, highlight model specifications underlying different types of deepfake generation methods, and contribute an accessible evaluation of the efficacy of existing detection methods. We highlight the limitations of existing detection mechanisms and discuss the areas where policies and regulations are required to address the challenges of deepfakes.
  arXiv  Detail & Related papers  (2024-06-20T13:15:54Z)
• Finding the white male: The prevalence and consequences of algorithmic gender and race bias in political Google searches [0.0]
  This article proposes and tests a framework of algorithmic representation of minoritized groups in a series of four studies. First, two algorithm audits of political image searches delineate how search engines reflect and uphold structural inequalities by under- and misrepresenting women and non-white politicians. Second, two online experiments show that these biases in algorithmic representation in turn distort perceptions of the political reality and actively reinforce a white and masculinized view of politics.
  arXiv  Detail & Related papers  (2024-05-01T05:57:03Z)
• KHAN: Knowledge-Aware Hierarchical Attention Networks for Accurate Political Stance Prediction [37.261840137596195]
  We propose a novel knowledge-aware approach to political stance prediction (KHAN) We employ (1) hierarchical attention networks (HAN) to learn the relationships among words and sentences in three different levels and (2) knowledge encoding (KE) to incorporate external knowledge for real-world entities into the process of political stance prediction. Through extensive evaluations on three real-world datasets, we demonstrate the superiority of DASH in terms of (1) accuracy, (2) efficiency, and (3) effectiveness.
  arXiv  Detail & Related papers  (2023-02-23T16:09:42Z)
• PAR: Political Actor Representation Learning with Social Context and Expert Knowledge [45.215862050840116]
  We propose textbfPAR, a textbfPolitical textbfActor textbfRepresentation learning framework. We retrieve and extract factual statements about legislators to leverage social context information. We then construct a heterogeneous information network to incorporate social context and use relational graph neural networks to learn legislator representations.
  arXiv  Detail & Related papers  (2022-10-15T19:28:06Z)
• Entity Graph Extraction from Legal Acts -- a Prototype for a Use Case in Policy Design Analysis [52.77024349608834]
  This paper presents a prototype developed to serve the quantitative study of public policy design. Our system aims to automate the process of gathering legal documents, annotating them with Institutional Grammar, and using hypergraphs to analyse inter-relations between crucial entities.
  arXiv  Detail & Related papers  (2022-09-02T10:57:47Z)
• Requirements for Open Political Information: Transparency Beyond Open Data [48.7576911714538]
  We conduct user interviews to identify wants and needs among stakeholders. We use this information to sketch out the foundational requirements for a functional political information technical system.
  arXiv  Detail & Related papers  (2021-12-06T15:42:03Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.