Aug 26, 2024

BBC News Impersonator

Kyal Pindolia, Funmi “Finn” Okuleye

Details

Summary

This paper presents a demonstration that showcases the current capabilities of AI models to imitate genuine news outlets, using BBC News as an example. The demo allows users to generate a realistic-looking article, complete with a headline, image, and text, based on their chosen prompts. The purpose is to viscerally illustrate the potential risks associated with AI-generated misinformation, particularly how convincingly AI can mimic trusted news sources.

See Code

Download PDF

View Presentation

Cite this work:

@misc {

title={

BBC News Impersonator

author={

Kyal Pindolia, Funmi “Finn” Okuleye

date={

8/26/24

organization={Apart Research},

note={Research submission to the research sprint hosted by Apart.},

howpublished={https://apartresearch.com}

}

Review

Reviewer Name

Constructive critique

Criteria 1:

Advancing interpretability (Possible questions to answer in this criteria: Does the project contribute to the field of mechanistic interpretability? Does it provide new insights into understanding or steering AI model behavior? How well does it move us towards reprogramming AI models? How original and innovative is the approach?)

2.5

Criteria 2:

Relevance for AI safety (Possible questions to answer in this criteria: How important is the contribution to advancing the field of AI safety? Do we expect the results to generalize beyond the specific case(s) presented in the submission? Does the approach introduce new safety mechanisms or enhance existing ones in innovative ways?))

2.5

Criteria 3:

Methodology & presentation (Possible questions to answer in this criteria: How well is the project executed from a technical standpoint?, Is the code well-structured, documented, and reproducible?, How effectively does it utilize Goodfire's SDK/API and other provided resources?, How clearly and effectively is the research presented in the paper?, Quality of visualizations and demos (if applicable), Clarity of methodology explanation and results interpretation))

2.5

Reviewer's Comments

No reviews are available yet

Recent Projects

View All

Feb 20, 2025

Deception Detection Hackathon: Preventing AI deception

Mar 18, 2025

Safe ai

The rapid adoption of AI in critical industries like healthcare and legal services has highlighted the urgent need for robust risk mitigation mechanisms. While domain-specific AI agents offer efficiency, they often lack transparency and accountability, raising concerns about safety, reliability, and compliance. The stakes are high, as AI failures in these sectors can lead to catastrophic outcomes, including loss of life, legal repercussions, and significant financial and reputational damage. Current solutions, such as regulatory frameworks and quality assurance protocols, provide only partial protection against the multifaceted risks associated with AI deployment. This situation underscores the necessity for an innovative approach that combines comprehensive risk assessment with financial safeguards to ensure the responsible and secure implementation of AI technologies across high-stakes industries.

Mar 18, 2025