Jun 2, 2025

A Multidimensional Judge Model for Safe, Consistent and Ethical AI Orchestration

Anusha Asim, Ammar Ahmed Farooqi, Sergei Smirnov, Christopher Kinoshtia

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This project developed a multidimensional safety judge that transparently evaluates the outputs of large language models (LLMs) and AI routing systems across key safety dimensions: bias avoidance, factuality, manipulation resistance, and toxicity avoidance. By benchmarking both individual models and routed responses, the judge provides interpretable, auditable scores that highlight strengths, weaknesses and consistency issues. Our results show that intelligent routing, guided by these safety metrics, can deliver safer and more reliable AI outputs than monolithic models. This supports the development of trustworthy, ethical and socially responsible AI systems.

Cite this work:

@misc {

title={

@misc {

},

author={

Anusha Asim, Ammar Ahmed Farooqi, Sergei Smirnov, Christopher Kinoshtia

},

date={

6/2/25

},

organization={Apart Research},

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

howpublished={https://apartresearch.com}

}

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.