.ALign File

Jan 20, 2025

Fabari Agbora

Read Project

Details

Summary

In a post-AGI future, misaligned AI systems risk harmful consequences, especially with control over critical infrastructure. The Alignment Compliance Framework (ACF) ensures ethical AI adherence using .align files, Alignment Testing, and Decentralized Identifiers (DIDs). This scalable, decentralized system integrates alignment into development and lifecycle monitoring. ACF offers secure libraries, no-code tools for AI creation, regulatory compliance, continuous monitoring, and advisory services, promoting safer, commercially viable AI deployment.

Download PDF

View Presentation

Cite this work:

@misc {

title={

.ALign File

author={

Fabari Agbora

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

Shivam Raval

The proposal describes the Alignment Compliance Framework that ensures ethical AI adherence Alignment Testing, and Decentralized Identifiers as .align files in a post-AGI future. The proposed solution involves a compliance tracking system with several modules that align AI agents with human values, ensuring transparency, adaptability, and trustworthiness. The proposal relies on 2027 AGI prediction, I would be curious how the projected progress would change if an AGI scale system was released earlier than predicted. Furthermore, the reliance on GPT4 era models assumes that these systems would be capable of detecting alignment in an AGI model. Some details on experimental results by borrowing a setup configuration from oversight and control would strengthen the proposal.

C1:

C2:

2.5

C3:

2.5

Pablo Sanzo

The novel idea of a file standard for AI alignment compliance is interesting. I would have appreciated to learn more of the team's thoughts around how to deal with actors not following these compliance guidelines.

Also when it comes to the product and MVP, the doc could benefit from more detail on the possible go-to-market approaches, beyond partnering with big players.

C1:

C2:

2.5

C3:

Recent Projects

View All

Feb 20, 2025

Deception Detection Hackathon: Preventing AI deception

Mar 18, 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 18, 2025