Evaluating Argumentative Strategies in AI Debates

Data Science Master's Thesis (UBA) investigating argumentative strategies in debates between AI systems with asymmetric capabilities and cognitively limited judges.

🎯 Research Focus

This thesis explores how unrestricted AI agents exploit deceptive tactics to win debates against honest agents, and analyzes the impact of lower-capacity judges evaluating arguments from more advanced agents.

Key Research Questions

• How do asymmetric agent capabilities affect debate outcomes?
• What argumentative strategies emerge under different conditions?
• How does judge bias impact convergence towards truth?
• Can debate serve as an effective AI alignment technique?

🧪 Implementation: AI Safety via Debate on MNIST

The debate_mnist/ directory contains a complete implementation replicating Irving et al. (2018) with additional extensions:

• Asymmetric Debates: MCTS vs Greedy agent competitions
• Judge Evaluation: 8 different pixel selection strategies
• Bias Analysis: Precommit strategies and adversarial evaluation
• Logits Tracking: Progressive judge decision-making analysis

Quick Start

cd debate_mnist
python -m venv venv && source venv/bin/activate
pip install -r requirements.txt
python run_experiments.py  # Complete automation

📊 Key Contributions

1. Asymmetric Agent Analysis: First systematic study of mixed-capability debates
2. Judge Bias Quantification: Novel metrics for evaluating judge robustness
3. Strategic Behavior Emergence: Documentation of deceptive patterns in pixel selection
4. Scalable Evaluation Framework: Automated experimentation with comprehensive logging

📚 Theoretical Foundation

Based on recent advances in AI safety and alignment:

• AI Safety via Debate (Irving et al., 2018) - Core methodology
• Scalable AI Safety via Doubly-Efficient Debate (Khan et al., 2023)
• Measuring Progress on Scalable Oversight (Bowman et al., 2022)
• AI Control: Improving Safety Despite Intentional Subversion (Greenblatt et al., 2023)

📈 Expected Outcomes

• Quantitative analysis of debate dynamics under capability asymmetry
• Taxonomy of emergent argumentative strategies
• Framework for evaluating judge bias in AI systems
• Recommendations for debate-based alignment techniques

📬 Contact

Joaquín Salvador Machulsky Email: jmachulsky@dc.uba.ar