Grandfather Paradox in AI – Bias Mitigation & Ethical AI1

Nov 21, 2024

Grandfather Paradox in AI – Bias Mitigation & Ethical AI1

Maha Vishnu Sura

Read Project

Details

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.

Download PDF

View Presentation

Cite this work:

@misc {

title={

Grandfather Paradox in AI – Bias Mitigation & Ethical AI1

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}

}

Review

Reviewer Name

Constructive critique

Criteria 1:

Advancing interpretability (Possible questions to answer in this criteria: Does the project contribute to the field of mechanistic interpretability? Does it provide new insights into understanding or steering AI model behavior? How well does it move us towards reprogramming AI models? How original and innovative is the approach?)

2.5

Criteria 2:

Relevance for AI safety (Possible questions to answer in this criteria: How important is the contribution to advancing the field of AI safety? Do we expect the results to generalize beyond the specific case(s) presented in the submission? Does the approach introduce new safety mechanisms or enhance existing ones in innovative ways?))

2.5

Criteria 3:

Methodology & presentation (Possible questions to answer in this criteria: How well is the project executed from a technical standpoint?, Is the code well-structured, documented, and reproducible?, How effectively does it utilize Goodfire's SDK/API and other provided resources?, How clearly and effectively is the research presented in the paper?, Quality of visualizations and demos (if applicable), Clarity of methodology explanation and results interpretation))

2.5

Reviewer's Comments

Bharat

Clear and articulate problem with a plausible solution

The concepts of data validation, data provenance/ transparent, verifiable data handling all give a very good reason to use blockchain rather than being an ad-hoc addition for novelty value

Coluld benefit with detail on how smaller organizations or under-resourced sectors could adopt these measures without excessive cost. Including examples of pilot implementations would also be useful

C1:

4.5

C2:

C3:

Jaye Nias

This submission offers a valuable exploration of the intersection between philosophical theory and real-world AI challenges. However, the explanation of the Grandfather paradox could be expanded to better clarify its relevance to the proposed mitigation strategies. Additionally, the connection between the paradox and each strategy could be more thoroughly explained to strengthen the overall argument.

There are also some gaps in the discussion on how ethical and fairness metrics will be applied in technology solutions. Specifically, the proposed techniques could benefit from a more detailed explanation of how they will address potential feedback bias. While the importance of mitigating bias is acknowledged, there isn’t enough clarity on how the approach will handle data that is already influenced by societal biases. A more robust strategy for addressing these concerns would strengthen the submission. Overall, the submission demonstrates a strong understanding of the complexities at the intersection of AI and ethics, and with a bit more detail in the areas mentioned, it could be even more impactful.

C1:

C2:

2.5

C3:

Recent Projects

View All

Mar 24, 2025

Attention Pattern Based Information Flow Visualization Tool

Understanding information flow in transformer-based language models is crucial for mechanistic interpretability. We introduce a visualization tool that extracts and represents attention patterns across model components, revealing how tokens influence each other during processing. Our tool automatically identifies and color-codes functional attention head types based on established taxonomies from recent research on indirect object identification (Wang et al., 2022), factual recall (Chughtai et al., 2024), and factual association retrieval (Geva et al., 2023). This interactive approach enables researchers to trace information propagation through transformer architectures, providing deeper insights into how these models implement reasoning and knowledge retrieval capabilities.

Mar 24, 2025

jaime project Title

bbb

Mar 25, 2025

Safe ai

The rapid adoption of AI in critical industries like healthcare and legal services has highlighted the urgent need for robust risk mitigation mechanisms. While domain-specific AI agents offer efficiency, they often lack transparency and accountability, raising concerns about safety, reliability, and compliance. The stakes are high, as AI failures in these sectors can lead to catastrophic outcomes, including loss of life, legal repercussions, and significant financial and reputational damage. Current solutions, such as regulatory frameworks and quality assurance protocols, provide only partial protection against the multifaceted risks associated with AI deployment. This situation underscores the necessity for an innovative approach that combines comprehensive risk assessment with financial safeguards to ensure the responsible and secure implementation of AI technologies across high-stakes industries.

Mar 24, 2025