AI Ethics

A comprehensive study involving 1,100 members of the public and 119 AI experts reveals a fundamental divergence in value calculus: AI experts systematically downweight risks when judging the overall worth of AI systems, whereas the public remains highly sensitive to potential harms. ▶ Expert Risk Discounting: Experts exhibit a "utility-first" bias, effectively muting the impact of potential risks in their value judgments. This cognitive decoupling suggests a professional blind spot regarding social friction. ▶ Public Precautionary Logic: Unlike experts, the general public’s perception of AI value is heavily anchored in risk mitigation. For the layperson, a high-utility tool is often invalidated by even moderate risk profiles. Bagua Insight This "perception gap" is the root cause of the current regulatory and adoption friction. While the engineering community optimizes for benchmarks and functional potential, the public evaluates for existential and social safety. The industry is currently operating within a "bubble of optimism" where technical feasibility is mistaken for social viability. This study proves that "Alignment" isn't just a mathematical problem of making models follow instructions; it's a sociological problem of reconciling two entirely different risk-reward frameworks. If the gap persists, we risk a "Tech Backlash 2.0" where superior tech fails due to a deficit in perceived safety. Actionable Advice Product teams must move beyond technical transparency and embrace "Risk-Aware Design." Instead of trying to educate the public to think like experts, companies should integrate public risk tolerance as a hard constraint in the RLHF (Reinforcement Learning from Human Feedback) process. Shift the narrative from "What AI can do" to "How AI is bounded."