Grandfather Paradox in AI – Bias Mitigation & Ethical AI1

Maha Vishnu Sura

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.

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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}

}

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This work was done during one weekend by research workshop participants and does not represent the work of Apart Research.
This work was done during one weekend by research workshop participants and does not represent the work of Apart Research.