Mar 10, 2025
Identification if AI generated content
Bakyt Naurzalinov, Ivan Bondar
Our project falls within the Social Sciences track, focusing on the identification of AI-generated text content and its societal impact. A significant portion of online content is now AI-generated, often exhibiting a level of quality and human-likeness that makes it indistinguishable from human-created content. This raises concerns regarding misinformation, authorship transparency, and trust in digital communication.
Nakshathra Suresh
This submission was very interesting to read and engages all aspects of the social sciences track, which was refreshing to see. The authors should be commended for their ability to conduct novel research and apply these frameworks to their submission. However, there was little examination of existing literature, which meant the foundation of this paper was rooted in a lack of evidence-based research. The impact assessment could have been fleshed out in more detail to understand the risks of particular populations that might be impacted even further by the AI-generated content, therefore contributing to a greater understanding of the risks of AI in society. Overall, a fantastic effort.
Ziba Atak
Strengths:
- Relevance: The topic is highly relevant, addressing the societal impact of AI-generated text and the need for detection tools.
-Identification of Challenges: The paper effectively identifies key challenges, such as misinformation and authorship transparency.
-Ethical Considerations: The discussion of ethical concerns and the importance of critical thinking in digital media is valuable.
Areas for Improvement:
-Include more citations to demonstrate engagement with existing literature.
-Provide a detailed methodology section explaining how the software tool was developed and tested.
-Propose actionable recommendations or novel insights based on the findings.
-Suggest concrete solutions or mitigation strategies for the challenges identified.
-Expand the analysis of limitations and potential negative consequences.
-Clearly document the methodology and provide access to the software tool for reproducibility.
-Increase the number of test cases to draw more reliable conclusions.
-Expand the discussion to include your own perspective, recommendations, and future research directions.
Suggestions for Future Work:
-Conduct a more comprehensive study with a larger dataset and detailed methodology.
-Explore cross-disciplinary perspectives (e.g., ethics, policy) to broaden the scope and impact.
Cecilia Elena Tilli
I think it would have been helpful to focus on a more specific threat, to be able to make the project more targeted/focused. As it is written now, it is pretty high level - I would have loved to see it more clearly spelled out for a specific case how the inability to determine of a specific piece of content was AI generated would cause significant harm.
I'm somewhat skeptical of the long term feasibility of this kind of approach (reliably judging how content was created based on the content) as opposed to e.g. some kind of verification for especially important content.
A couple of specific points:
"Studies in psychology and sociology highlight
the cognitive biases that affect human perception of truth, showing that people tend to trust content based on its presentation rather than its source (Pennycook & Rand, 2019). This makes the inability to distinguish AI-generated content a potential risk to information integrity and social trust."
I would challenge this conclusion? I do not see how sentence 1 logically leads to sentence 2?
"By enhancing the ability to identify AI-generated content, we contribute to AI safety by promoting transparency, accountability, and informed decision-making"
The theory of change here is quite fuzzy
Cite this work
@misc {
title={
@misc {
},
author={
Bakyt Naurzalinov, Ivan Bondar
},
date={
3/10/25
},
organization={Apart Research},
note={Research submission to the research sprint hosted by Apart.},
howpublished={https://apartresearch.com}
}