機械学習の安全性保証のための因果モデル
A causal model of safety assurance for machine learning
ML応用の安全性保証ケースを構築するため、安全工学の原則に基づく因果モデルのフレームワークを提案し、4種類の証拠と構造化分析手法を定義した論文。
著者: Simon Burton
分類: cs.SE, cs.LG, cs.RO
原文アブストラクト
This paper proposes a framework based on a causal model of safety upon which effective safety assurance cases for ML-based applications can be built. In doing so, we build upon established principles of safety engineering as well as previous work on structuring assurance arguments for ML. The paper defines four categories of safety case evidence and a structured analysis approach within which these evidences can be effectively combined. Where appropriate, abstract formalisations of these contributions are used to illustrate the causalities they evaluate, their contributions to the safety argument and desirable properties of the evidences. Based on the proposed framework, progress in this area is re-evaluated and a set of future research directions proposed in order for tangible progress in this field to be made.
