Mar 10, 2025

AI Society Tracker

Abigail Yohannes

Details

Details

Arrow
Arrow
Arrow

Summary

My project aimed to develop a platform for real time and democratized data on ai in society

Cite this work:

@misc {

title={

AI Society Tracker

},

author={

Abigail Yohannes

},

date={

3/10/25

},

organization={Apart Research},

note={Research submission to the research sprint hosted by Apart.},

howpublished={https://apartresearch.com}

}

Review

Review

Arrow
Arrow
Arrow

Reviewer's Comments

Reviewer's Comments

Arrow
Arrow
Arrow

Ziba Atak

Strengths:

- Novel Idea: The concept of a real-time AI societal impact tracker is innovative and addresses a critical gap in AI safety and policy.

-Relevance: The paper highlights the importance of tracking AI’s societal impact, particularly in areas like job markets, ethics, and policymaking.

-Groundwork: A strong foundation for future work, including identifying data sources and outlining the need for such a tool.

Areas for Improvement:

-Methodology and Documentation: The paper lacks a methodology section and detailed documentation (due to time constraints), making it difficult to evaluate the technical implementation. Include a clear explanation of how the tracker works and provide access to the codebase.

-Results and Insights: The results section is missing due to time constraints, and the app does not provide enough information to interpret the data. Include a detailed analysis of the findings and their implications.

-Limitations and Risks: The paper does not thoroughly analyze the limitations of the tracker itself or the potential risks of implementing such a tool. Expand on these aspects to strengthen the analysis.

-Scalability and Generalization: Demonstrate how the tracker could be scaled or generalized to broader applications. Include a discussion of future steps and scalability.

Suggestions for Future Work:

-Develop a detailed methodology and document the technical implementation, including access to the codebase.

-Conduct a thorough analysis of the results and their implications for AI safety and policy.

-Explore mitigation strategies for the risks and limitations.

-Investigate the tracker’s performance in diverse contexts and expand the data collection process.

C1:

3

C2:

3

C3:

2

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.

Read More

Mar 24, 2025

jaime project Title

bbb

Read More

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.

Read More

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.