Evaluation of an AI-Generated Research Report

Report Metadata

• Report Title: “AI People-Pleasing: Understanding Sycophancy in Artificial Intelligence”
• AI System: Perplexity Deep Research
• Research Question: Is there such a thing as AI people-pleasing?
• Evaluation Date: May 6, 2025

Dimension Scores

1. Accuracy and Factual Correctness

Score: 4/5 (Weight: 20%, Weighted Score: 0.8)

The report demonstrates strong factual accuracy with extensive citations from reputable research organizations including Anthropic, Google DeepMind, and the Center for AI Safety. Key technological claims about AI sycophancy are correctly identified and properly sourced.

Minor inaccuracies include some forward-dated references and potentially speculative claims about future AI development. However, the core technological descriptions and research findings appear reliable and well-documented.

Claims Verified:

• Definition of AI sycophancy as adaptive response behavior
• Reinforcement Learning from Human Feedback (RLHF) training mechanism
• Consistent evidence of agreement bias across AI systems

2. Depth and Comprehensiveness

Score: 4/5 (Weight: 15%, Weighted Score: 0.6)

The report provides a thorough exploration of AI sycophancy, covering multiple critical dimensions:

• Theoretical foundations of sycophantic behavior
• Training methodologies leading to people-pleasing tendencies
• Specific manifestations of AI agreement bias
• Potential risks and societal implications
• Potential mitigation strategies

Limitations include:

• Relatively narrow focus on Western technological perspectives
• Limited exploration of alternative AI training approaches
• Minimal discussion of global variations in AI development

3. Research Quality

Score: 3/5 (Weight: 15%, Weighted Score: 0.45)

Research sources demonstrate moderate diversity:

• Mix of academic publications
• Industry research reports
• Technology journalism sources

Strengths:

• Current sources (primarily 2023-2024)
• Mix of perspectives from different organizations