パラダイムシフト
私たちは 「どんな犠牲を払っても性能を追求する」 という考え方から、 Responsible AI (RAI) framework. In this new era, technical success is strictly contingent upon ethical robustness and safety guardrails.
1. Constrained Optimization
Historically, the goal was minimizing a loss function $L(\theta)$. The new paradigm treats AI as a constrained optimization problem: $$\max P \text{ subject to } C_1, C_2, \dots, C_n$$ where $C$ represents non-negotiable safety and fairness thresholds.
2. The "In-Vitro" vs. "In-Vivo" Gap
Models often achieve state-of-the-art (SOTA) results on static benchmarks (in-vitro) but exhibit catastrophic failures in real-world sociotechnical environments (in-vivo) due to unforeseen interactions.
Left: High accuracy/speed, zero safety/transparency. Right: Balanced hexagon representing safety, fairness, and interpretability.
Example: High-Frequency Trading
A performance-only model succeeds if it maximizes ROI. An RAI model is a failure if it achieves high ROI but triggers a "flash crash" due to lack of market stability safeguards.