Table of Contents
Fetching ...

The Missing Variable: Socio-Technical Alignment in Risk Evaluation

Niclas Flehmig, Mary Ann Lundteigen, Shen Yin

TL;DR

This paper introduces a novel socio-technical alignment $STA$ variable designed to be integrated into the foundational risk equation, which estimates the degree of harmonious interaction between the AI systems, human operators, and organizational processes.

Abstract

This paper addresses a critical gap in the risk assessment of AI-enabled safety-critical systems. While these systems, where AI systems assists human operators, function as complex socio-technical systems, existing risk evaluation methods fail to account for the associated complex interaction between human, technical, and organizational elements. Through a comparative analysis of system attributes from both socio-technical and AI-enabled systems and a review of current risk evaluation methods, we confirm the absence of socio-technical considerations in standard risk expressions. To bridge this gap, we introduce a novel socio-technical alignment $STA$ variable designed to be integrated into the foundational risk equation. This variable estimates the degree of harmonious interaction between the AI systems, human operators, and organizational processes. A case study on an AI-enabled liquid hydrogen bunkering system demonstrates the variable's relevance. By comparing a naive and a safeguarded system design, we illustrate how the $STA$-augmented expression captures socio-technical safety implications that traditional risk evaluation overlooks, providing a more holistic basis for risk evaluation.

The Missing Variable: Socio-Technical Alignment in Risk Evaluation

TL;DR

This paper introduces a novel socio-technical alignment variable designed to be integrated into the foundational risk equation, which estimates the degree of harmonious interaction between the AI systems, human operators, and organizational processes.

Abstract

This paper addresses a critical gap in the risk assessment of AI-enabled safety-critical systems. While these systems, where AI systems assists human operators, function as complex socio-technical systems, existing risk evaluation methods fail to account for the associated complex interaction between human, technical, and organizational elements. Through a comparative analysis of system attributes from both socio-technical and AI-enabled systems and a review of current risk evaluation methods, we confirm the absence of socio-technical considerations in standard risk expressions. To bridge this gap, we introduce a novel socio-technical alignment variable designed to be integrated into the foundational risk equation. This variable estimates the degree of harmonious interaction between the AI systems, human operators, and organizational processes. A case study on an AI-enabled liquid hydrogen bunkering system demonstrates the variable's relevance. By comparing a naive and a safeguarded system design, we illustrate how the -augmented expression captures socio-technical safety implications that traditional risk evaluation overlooks, providing a more holistic basis for risk evaluation.

Paper Structure