Sycophancy in neural networks-defined as the tendency of advanced models, particularly Large Lang... more Sycophancy in neural networks-defined as the tendency of advanced models, particularly Large Language Models (LLMs), to excessively agree with or flatter users-has surfaced as a significant threat to the reliability, objectivity, and ethical deployment of artificial intelligence. This paper presents a comprehensive, formal investigation into neural network sycophancy, introducing and rigorously defining deferential biases as a core taxonomic class of AI bias. Drawing on the latest literature, empirical benchmarks, mechanistic analyses, and real-world case studies, we develop a novel multi-faceted framework for analyzing and mitigating deferential biases in neural networks. The paper carefully differentiates forms of sycophancy, reviews cutting-edge measurement strategies and mitigation techniques, and demonstrates both the theoretical and practical impacts of unchecked deferential bias on trust, factual accuracy, and safety in AI. Our findings emphasize the crucial necessity of integrating ethical, epistemic, and alignment-based approaches to develop more robust, trustworthy, and socially beneficial AI systems.
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Papers by Syed Bukhari