How is artificial intelligence being used in journalism now? What's coming next?

Oct 11, 2024·
Jason Lucas
Jason Lucas
· 2 min read
WXXI Connections Studio
Abstract
In this comprehensive radio discussion, we examine the dual nature of artificial intelligence in modern journalism - both as a tool for newsroom efficiency and as a vector for sophisticated disinformation campaigns. Jason Lucas presents research on AI-generated disinformation during crisis situations, highlighting how bad actors exploit critical moments when communities are most vulnerable to false information. The conversation covers the evolving landscape of AI applications in journalism, from automated content generation to fact-checking systems, while addressing the urgent need for robust detection mechanisms. Felix Simon contributes insights from four years of research into AI adoption in newsrooms, providing context for how the industry is adapting to these technological changes. Together, we explore the challenges facing journalists, policymakers, and technologists in maintaining information integrity while harnessing AI’s beneficial capabilities.
Location

WXXI Public Media

280 State Street, Rochester, New York 14614

event
Note
Click on the Listen link above to hear the full 52-minute radio discussion on WXXI’s website.

Program Overview

This episode of WXXI’s “Connections” brings together leading researchers to examine artificial intelligence’s transformative impact on journalism and its potential for misuse in disinformation campaigns.

Key Discussion Points

AI in Newsrooms Today

  • Content automation and efficiency improvements
  • Fact-checking systems and verification tools
  • Editorial decision support and audience analytics
  • Challenges in implementation across different newsroom sizes

The Disinformation Threat

  • Crisis exploitation: How bad actors target vulnerable moments
  • Sophisticated generation: Advanced AI creating convincing false content
  • Detection challenges: The arms race between generation and detection
  • Community impact: Real-world consequences during emergencies

Research Insights

Jason Lucas presents findings on:

  • Longtail events: High-impact, resource-limited scenarios where disinformation causes maximum harm
  • Detection evasion: How AI-generated content is becoming harder to identify
  • Multilingual campaigns: Cross-linguistic disinformation strategies
  • Policy implications: Recommendations for media organizations and regulators

The discussion draws from recent academic work including:

  • Studies on expert-edited machine-generated outputs and detection challenges
  • Analysis of multilingual disinformation campaigns
  • Research on longtail impacts of generative AI in crisis situations

Collaborative Perspectives

Felix M. Simon

Research fellow at the Reuters Institute for the Study of Journalism, University of Oxford, sharing insights from “Artificial Intelligence in the News” - a comprehensive four-year study of AI adoption in journalism.

Jason Lucas

Ph.D. student in Informatics at Penn State University, presenting research on AI-driven disinformation tactics and their exploitation during crisis events.

Program Innovation

The show features an AI-generated podcast segment discussing Simon’s research paper, demonstrating both the capabilities and potential concerns around synthetic media in journalism contexts.

Host

David Streever guest hosts this episode, bringing his background in journalism and public media to facilitate this crucial conversation about technology’s role in information integrity.

Listen to the full discussion on WXXI’s “Connections” to hear detailed insights on navigating the complex intersection of artificial intelligence, journalism, and public trust.

AI Journalism Disinformation Crisis Communication Media Technology
Jason Lucas
Authors
Jason Lucas
Ph.D. Candidate in Informatics

I am a PhD candidate in Informatics in the College of IST at Penn State University, where I conduct research at the PIKE Research Lab under the guidance of Dr. Dongwon Lee. I specialize in AI/ML research focused on Information Integrity, Safe and Ethical AI, including combating harmful content across multiple languages and modalities. My research spans low-resource multilingual NLP, generative AI, and adversarial machine learning, with work extending across 79 languages. I have published 12 papers with 260+ citations in premier venues including ACL, EMNLP, IEEE, and NAACL.

My doctoral research focuses on bridging the digital language divide through transfer learning, classification (NLU), generation (NLG), adversarial attacks, and developing end-to-end AI pipelines using RAG and Agentic AI workflows for combating multilingual threats. Drawing from my Grenadian background and knowledge of local Creole languages, I bring a global perspective to AI challenges, working to democratize state-of-the-art AI capabilities for underserved linguistic communities worldwide. My mission is to develop robust multilingual multimodal systems and mitigate evolving security vulnerabilities while enhancing access to human language technology through cutting-edge solutions.

As an NSF LinDiv Fellow, I conduct transdisciplinary research advancing human-AI language interaction for social good. I actively mentor 5+ research interns and teach Applied Generative AI courses. Through industry experience at Lawrence Livermore National Lab, Interaction LLC, and Coalfire, I bridge academic research with practical applications in combating evolving security threats and enhancing global AI accessibility. I see multilingual advances and interdisciplinary collaboration as a competitive advantage, not a communication challenge. Beyond research, I stay active through dance, fitness, martial arts, and community service.