AI-Assisted Systematization for Evaluating GenAI Systems
Dhruv Agarwal, Emily Sheng, Chad Atalla, Jean Garcia-Gathright, Hussein Mozannar, Hannah Washington, Alexandra Chouldechova, Solon Barocas, Hanna Wallach
Read on arXiv →Key claim
AI can assist in systematizing evaluation concepts.
This paper addresses the challenge of evaluating generative AI systems by introducing AI-assisted systematization. It presents a structured representation of concepts and evaluates the quality of generated concept specs for hate-based rhetoric and digital empathy. The key result is that AI assistance can effectively support the systematization process, improving clarity in evaluation.
In plain English
This paper addresses the challenge of evaluating generative AI systems by introducing AI-assisted systematization. It presents a structured representation of concepts and evaluates the quality of generated concept specs for hate-based rhetoric and digital empathy. The key result is that AI assistance can effectively support the systematization process, improving clarity in evaluation.
The introduction of AI-assisted systematization for evaluating broad concepts is a meaningful extension of existing evaluation methods.
The paper provides a structured approach and validation methods, supporting its claims with evaluations of concept specs.
Deep reliability assessment
The methodology supports the feasibility of AI-assisted systematization but may overclaim the ease of achieving clarity and consistency in the outputs.
Reproducibility
No open source code or dataset is mentioned in the paper.
Discussion questions
- How does the AI-assisted systematization handle the inherent subjectivity in defining broad concepts like 'fairness' or 'creativity'?
- What are the practical implications for AI developers in terms of integrating these systematized concepts into their evaluation processes?
- What evidence would be required to demonstrate that AI-assisted systematization consistently produces better outcomes than manual systematization?
Key figure
Figure 2 illustrates the multi-agent systematizer architecture, showing the phases of contextualization, simulated expert discussion, and concept specification.