User Transparency Within AI
Jonathan King, Robert Hardy, Jeremiah Bailey, Amir Abdulgadir · Team Cool Team
Submitted to Howard University AI Safety Summit & Policy Hackathon. Projects from partner hackathons are early-stage work by participants, not Apart Research publications.
Generative AI technologies present immense opportunities but also pose significant challenges, particularly in combating misinformation and ensuring ethical use. This policy paper introduces a dual-output transparency framework requiring organizations to disclose AI-generated content clearly. The proposed system provides users with a choice between fact-based and mixed outputs, both accompanied by clear markers for AI generation. This approach ensures informed user interactions, fosters intellectual integrity, and aligns AI innovation with societal trust. By combining policy mandates with technical implementations, the framework addresses the challenges of misinformation and accountability in generative AI.
Reviews
I love this project because of x, y,z
The dual-output transparency framework presents a novel approach to AI content disclosure with clear implementation phases and metrics. Consider strengthening the technical specifications section with more concrete examples of how the marking system would work in practice.
Cite this project
@misc{king2024user,
title = {{User Transparency Within AI}},
author = {Jonathan King and Robert Hardy and Jeremiah Bailey and Amir Abdulgadir},
year = {2024},
month = nov,
note = {Submitted to Howard University AI Safety Summit \& Policy Hackathon, a partner hackathon},
howpublished = {\url{https://apartresearch.com/sprints/projects/user-transparency-within-ai}},
url = {https://apartresearch.com/sprints/projects/user-transparency-within-ai}
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