THE OKLAHOMA BAR JOURNAL 70 | MARCH 2026 Law Practice Tips By Julie Bays Cross-Examining Your AI: Sycophancy, Risks and Responsible Strategies for Legal Professionals ARTIFICIAL INTELLIGENCE (AI) has rapidly become an integral part of the legal profession. Large language models (LLMs) are now used for legal research, drafting documents, client communications and contract analysis. Thomson Reuters’ Future of Professionals Report 2024 found that 79% of law firm respondents expect AI to have a high or transformational impact on their work within five years.1 While AI offers substantial efficiency gains, many lawyers remain unaware of a fundamental risk: AI systems are not only capable of inventing legal citations and authority out of thin air, but they are also programmed to focus on satisfying the user, even if that means giving answers that are inaccurate or misleading. This tendency is built into how modern AI models are trained; they are rewarded for echoing the user’s assumptions and confirming their interpretations, rather than strictly adhering to legal facts. As a result, when a lawyer interacts with AI, the system may produce responses that sound authoritative but are not grounded in actual law, and it may do so simply to please the person asking the question. Recognizing this behavior is critical. Lawyers must approach AI-generated answers with skepticism and always verify their accuracy independently. This article explores the phenomenon of AI sycophancy, its risks for legal professionals and practical strategies for effective, ethical communication with AI tools. UNDERSTANDING AI SYCOPHANCY: OPTIMIZING USER SATISFACTION OVER TRUTH Modern generative AI systems are not fundamentally designed to produce objectively correct answers. Instead, they are optimized to maximize user satisfaction, often rewarding responses that echo user assumptions, validate their phrasing or confirm their interpretations. This tendency, known as AI sycophancy, results in models that pursue human approval, sometimes at the expense of factual accuracy.2 At their core, LLMs are statistical models trained on massive datasets to predict the most likely next word in a sequence. After that base training, many systems are fine-tuned using human feedback. People rate sample outputs with a thumbs up or thumbs down, and that feedback steers future model behavior. Human feedback during training, such as thumbs-up ratings, steers AI behavior toward agreeableness and flattery. Researchers have documented that models optimized in this way show a marked predilection for sycophantic answers, aligning responses with user preferences rather than objective truth. A notable incident occurred in April 2025 when OpenAI released an update to its GPT-4o model. Intended to make the system warmer and more supportive, the update resulted in the model becoming excessively flattering and agreeable, even validating dubious statements. OpenAI acknowledged the issue and rolled back the update within four days, admitting that the model had skewed too heavily toward short-term human feedback. This episode illustrates how easily AI systems can tilt toward agreement, even when developers intend balanced outputs.3
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