Mar 10, 2025

An Interpretable Classifier based on Large scale Social Network Analysis

Monojit Banerjee

Details

Details

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Summary

Mechanistic model interpretability is essential to understand AI decision making, ensuring safety, aligning with human values, improving model reliability and facilitating research. By revealing internal processes, it promotes transparency, mitigates risks, and fosters trust, ultimately leading to more effective and ethical AI systems in critical areas. In this study, we have explored social network data from BlueSky and built an easy-to-train, interpretable, simple classifier using Sparse Autoencoders features. We have used these posts to build a financial classifier that is easy to understand. Finally, we have visually explained important characteristics.

Cite this work:

@misc {

title={

An Interpretable Classifier based on Large scale Social Network Analysis

},

author={

Monojit Banerjee

},

date={

3/10/25

},

organization={Apart Research},

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

howpublished={https://apartresearch.com}

}

Review

Review

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Reviewer's Comments

Reviewer's Comments

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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.