議論する機械：人命に関わる意思決定を行うブラックボックスAIへの人間による監督
Arguing Machines: Human Supervision of Black Box AI Systems That Make Life-Critical Decisions
独立に訓練した2つのAIの不一致を人間が監督することで、ブラックボックスAIの判断精度を向上させる枠組みを提案し、画像分類と自動運転データで有効性を示した。
著者: Lex Fridman, Li Ding, Benedikt Jenik, Bryan Reimer
分類: cs.AI, cs.RO
原文アブストラクト
We consider the paradigm of a black box AI system that makes life-critical decisions. We propose an "arguing machines" framework that pairs the primary AI system with a secondary one that is independently trained to perform the same task. We show that disagreement between the two systems, without any knowledge of underlying system design or operation, is sufficient to arbitrarily improve the accuracy of the overall decision pipeline given human supervision over disagreements. We demonstrate this system in two applications: (1) an illustrative example of image classification and (2) on large-scale real-world semi-autonomous driving data. For the first application, we apply this framework to image classification achieving a reduction from 8.0% to 2.8% top-5 error on ImageNet. For the second application, we apply this framework to Tesla Autopilot and demonstrate the ability to predict 90.4% of system disengagements that were labeled by human annotators as challenging and needing human supervision.
