[Paper Review] An intelligent sociotechnical systems (iSTS) framework: Enabling a hierarchical human-centered AI (hHCAI) approach

Wei Xu, Zaifeng Gao|arXiv (Cornell University)|Jan 6, 2024

Ethics and Social Impacts of AI4 citations

TL;DR

This paper proposes an intelligent sociotechnical systems (iSTS) framework that extends traditional sociotechnical systems theory to address AI's societal impacts by integrating human-centered AI (HCAI) across individual, organizational, ecosystem, and societal levels. It introduces a hierarchical HCAI (hHCAI) approach to enable structured, joint optimization of human and AI performance, offering a scalable, sociotechnically grounded solution to current HCAI limitations.

ABSTRACT

While artificial intelligence (AI) offers significant benefits, it also has negatively impacted humans and society. A human-centered AI (HCAI) approach has been proposed to address these issues. However, current HCAI practices have shown limited contributions due to a lack of sociotechnical thinking. To overcome these challenges, we conducted a literature review and comparative analysis of sociotechnical characteristics with respect to AI. Then, we propose updated sociotechnical systems (STS) design principles. Based on these findings, this paper introduces an intelligent sociotechnical systems (iSTS) framework to extend traditional STS theory and meet the demands with respect to AI. The iSTS framework emphasizes human-centered joint optimization across individual, organizational, ecosystem, and societal levels. The paper further integrates iSTS with current HCAI practices, proposing a hierarchical HCAI (hHCAI) approach. This hHCAI approach offers a structured approach to address challenges in HCAI practices from a broader sociotechnical perspective. Finally, we provide recommendations for future iSTS and hHCAI work.

Motivation & Objective

• To address the limitations of current human-centered AI (HCAI) practices, which often lack integration with sociotechnical systems thinking.
• To identify gaps in existing HCAI approaches by analyzing sociotechnical characteristics in AI contexts.
• To develop updated sociotechnical systems (STS) design principles tailored for AI-driven environments.
• To propose an intelligent sociotechnical systems (iSTS) framework that enables human-centered joint optimization across multiple levels: individual, organizational, ecosystem, and societal.
• To integrate iSTS with HCAI practices by introducing a hierarchical HCAI (hHCAI) approach for systematic, scalable implementation.

Proposed method

• Conducting a comprehensive literature review and comparative analysis of sociotechnical characteristics in AI applications.
• Deriving updated sociotechnical systems (STS) design principles through synthesis of existing frameworks and AI-specific challenges.
• Developing the iSTS framework as an extension of traditional STS theory, embedding human-centered design across four hierarchical levels: individual, organizational, ecosystem, and societal.
• Integrating the iSTS framework with current HCAI practices to propose the hierarchical HCAI (hHCAI) approach, which structures human-AI collaboration across levels.
• Using a systems thinking methodology to ensure joint optimization of human and AI performance at each level of the hierarchy.
• Providing actionable recommendations for future research and implementation of iSTS and hHCAI in real-world AI deployments.

Experimental results

Research questions

• RQ1 How can sociotechnical systems theory be extended to better address the complexities of AI integration in human societies?
• RQ2 What are the key sociotechnical characteristics that differentiate traditional STS from AI-enabled environments?
• RQ3 How can human-centered AI (HCAI) be systematically scaled across individual, organizational, ecosystem, and societal levels?
• RQ4 What design principles are necessary to enable joint human-AI optimization in AI-driven sociotechnical systems?
• RQ5 How can a hierarchical structure enhance the scalability and effectiveness of HCAI practices in complex systems?

Key findings

• The iSTS framework successfully extends traditional STS theory by embedding human-centered design across four hierarchical levels: individual, organizational, ecosystem, and societal.
• The proposed hHCAI approach enables structured, scalable human-AI collaboration by organizing HCAI practices into a multi-level hierarchy.
• Updated sociotechnical systems (STS) design principles were identified and integrated into the iSTS framework to better align with AI-specific sociotechnical dynamics.
• The integration of iSTS with HCAI practices provides a systematic method for joint optimization of human and AI performance across diverse system levels.
• The framework offers a practical pathway for future research and implementation of human-centered AI in complex, large-scale systems.
• The study provides actionable recommendations for advancing iSTS and hHCAI research, particularly in interdisciplinary and real-world AI deployment contexts.