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Summary
The Grandfather Paradox in Artificial Intelligence (AI) describes a
self-perpetuating cycle where outputs from flawed AI models re-
enter the training process, leading to recursive degradation of
model performance, ethical inconsistencies, and amplified biases.
This issue poses significant risks, particularly in high-stakes
domains such as healthcare, criminal justice, and finance.
This memorandum analyzes the paradox’s origins, implications,
and potential solutions. It emphasizes the need for iterative data
verification, dynamic feedback control systems, and cross-system
audits to maintain model integrity and ensure compliance with
ethical standards. By implementing these measures, organizations
can mitigate risks, enhance public trust, and foster sustainable AI
development.
Cite this work:
@misc {
title={
@misc {
},
author={
Maha Vishnu Sura
},
date={
11/21/24
},
organization={Apart Research},
note={Research submission to the research sprint hosted by Apart.},
howpublished={https://apartresearch.com}
}
