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Computer Science > Artificial Intelligence

arXiv:2404.03502 (cs)
[Submitted on 4 Apr 2024]

Title:AI and the Problem of Knowledge Collapse

Authors:Andrew J. Peterson
View a PDF of the paper titled AI and the Problem of Knowledge
Collapse, by Andrew J. Peterson
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    Abstract:While artificial intelligence has the potential to
    process vast amounts of data, generate new insights, and unlock
    greater productivity, its widespread adoption may entail
    unforeseen consequences. We identify conditions under which AI,
    by reducing the cost of access to certain modes of knowledge, can
    paradoxically harm public understanding. While large language
    models are trained on vast amounts of diverse data, they
    naturally generate output towards the 'center' of the
    distribution. This is generally useful, but widespread reliance
    on recursive AI systems could lead to a process we define as
    "knowledge collapse", and argue this could harm innovation and
    the richness of human understanding and culture. However, unlike
    AI models that cannot choose what data they are trained on,
    humans may strategically seek out diverse forms of knowledge if
    they perceive them to be worthwhile. To investigate this, we
    provide a simple model in which a community of learners or
    innovators choose to use traditional methods or to rely on a
    discounted AI-assisted process and identify conditions under
    which knowledge collapse occurs. In our default model, a 20%
    discount on AI-generated content generates public beliefs 2.3
    times further from the truth than when there is no discount.
    Finally, based on the results, we consider further research
    directions to counteract such outcomes.

Comments:    16 pages, 7 figures
Subjects:    Artificial Intelligence (cs.AI); Computers and Society
             (cs.CY)
ACM classes: I.2.0
Cite as:     arXiv:2404.03502 [cs.AI]
             (or arXiv:2404.03502v1 [cs.AI] for this version)
             https://doi.org/10.48550/arXiv.2404.03502
             Focus to learn more
             arXiv-issued DOI via DataCite

Submission history

From: Andrew Peterson [view email]
[v1] Thu, 4 Apr 2024 15:06:23 UTC (57 KB)
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