HITL For High Risk AI Domains

Jan 20, 2025

HITL For High Risk AI Domains

Tyler Edwards, Sruthi Kuriakose, Subramanyam Sahoo, Shamith Achanta

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Summary

Our product addresses the challenge of aligning AI systems with the legal, ethical, and policy frameworks of high-risk domains like healthcare, defense, and finance by integrating a flexible human-in-the-loop (HITL) system. This system ensures AI outputs comply with domain-specific standards, providing real-time explainability, decision-level accountability, and ergonomic decision support to empower experts with actionable insights.

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Cite this work:

@misc {

title={

HITL For High Risk AI Domains

author={

Tyler Edwards, Sruthi Kuriakose, Subramanyam Sahoo, Shamith Achanta

date={

1/20/25

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

Ricardo Raphael Corona Moreno

The proposal recognizes the crucial role of human oversight in high-stakes AI applications. However, from my experience with AI projects at Lanai and Traverse 3D, I know that human-in-the-loop systems often struggle with scalability and consistency across different operators. (1) The technical foundation is solid but more detail is needed on how to maintain HITL effectiveness at scale.

(2) The focus on high-risk domains and regulatory compliance addresses important safety needs. Clear threat model around misaligned AI decisions.

(3) Their pilot experiments show promise but need more rigorous validation in real-world settings.

C1:

3.7

C2:

4.1

C3:

3.8

Jaime Raldua

I think this project has some interesting technical components and a solid implementation approach, but I have some concerns about its broader impact on AI safety. The focus on high-risk domains like healthcare and defense is practical, but it doesn't really tackle the core AI safety challenges we'll face with more advanced AI systems.

The human-in-the-loop system with reward modeling, clustering, and explainable AI is well thought out technically, and I like how they backed up their concerns with real-world AI failure examples. However, how will this system stay competitive against faster, more economically viable AI systems that don't use human oversight? This is important because if it's too slow or expensive, organizations might just skip the safety measures.

C1:

2.7

C2:

1.2

C3:

2.9

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