Multi-Level Sociotechnical Systems Approach to Human-Centered AI
Citation: Jordan Richard Schoenherr, Katina Michael, Multi-Level Sociotechnical Systems Approach to Human-Centered AI, ed. Wei Xu, Handbook of Human-Centered Artificial Intelligence, Springer Nature Singapore, pp. 119-192.
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Contents
1 Introduction .................................................................................. 121
1.1 Centering the Design Process on Humans ............................................ 121
1.2 Overview .... . .. . .. . .. .. . .. . .. . .. . .. .. . .. . .. . .. . .. . .. .. . .. . .. . .. . .. .. . .. . .. . .. . .. . .. .. . 123
2 Sociotechnical Theory: Evolution, Foundation Principles, and Core Features ...... ...... 124
2.1 Theoretical Foundations ............................................................... 124
2.2 System Definition and Core Features ................................................. 126
2.3 Sociotechnical Systems in an Era of AI .............................................. 134
3 Human-Centered Design for Sociotechnical Systems ...................................... 137
3.1 Methodological Linkages: Human-Centered Design and STS ....................... 137
3.2 From Intraorganizational to Complex Multi-boundary Systems ..................... 138
3.3 Human-Centered Design Process for Sociotechnical Systems .... . .................. 140
3.4 Human Factor Integration ............................................................. 141
3.5 Human Factor and Ergonomics ....................................................... 142
4 Participatory Design: Challenges and Innovative Practice ................................. 145
4.1 The Complexity of Stakeholder Engagement ......... .................. ............. 145
4.2 Evolution and Challenges of Participatory Systems Design .................... ..... 146
4.3 Organizational Barriers to Participatory Design ...................................... 147
4.4 Inclusive Design and the Marginalization of Some Stakeholders ............. ...... 148
5 Analytic Frameworks Multilevel STS ...................................................... 149
5.1 Micro-level: Foundational Intrapersonal and Interpersonal Processes .... ........... 149
5.2 Meso-level: Group and Organizational ............................................... 153
5.3 Macro-level: Regional, National, and International Innovation and Sociotechnical Innovation Systems ................................................................... 157
5.4 Sector-Based Use Cases of AI-STS ........................ .......................... 159
5.5 Translational Processes Across AI-STS .............................................. 165
6 Human-Centered Design Cognitive Principles for STS .................................... 166
6.1 Limitations of Cognitive Processes and Representations Must Be Accommodated ........................................................................ 167
6.2 Situational Awareness Must Focus on Affordances .................................. 168
6.3 Trust Must Be Calibrated and Errors Identified and Managed ....................... 169
6.4 Social-Cognitive Processes Function Optimally When Explicitly Defined .......... 169
6.5 Networks That Define STS Must Be Clearly Mapped ............................... 170
6.6 Distributed Situational Awareness Should Be Measured Continuously ............. 171
6.7 Formalized Monitoring and Regulation Mechanisms Are Necessary for Stability, Accountability, and Control ........................................................... 171
6.8 Clear Performance Indicators Are Required for Adaptive Coordination and Oversight .......................................................................... 172
6.9 Distributed Situational Awareness of Macro-level Systems Will Create Fragmented Mental Models Requiring Action ....................................... 172
7 Future Directions: Evolving Sociotechnical Systems in the Age of AI .................... 173
7.1 Unanswered Research Questions ..................................................... 173
7.2 Emerging AI Capabilities and STS Implications .... . .. .. .. .. .. .. . .. .. .. .. .. .. . .. .. .. 174
7.3 Future Implications .................................................................... 177
7.4 Toward Responsible AI-STS Innovation ............................................. 177
8 Conclusion ................................................................................... 178
References ....................................................................................... 179
Abstract
This chapter develops a sociotechnical systems (STS) approach to artificial intelligence (AI). Extending principles from human-centered design (HCD), human factors, and human-centered AI (HCAI), this chapter articulates design practices capable of addressing the complexity, opacity, and scale of modern multilevel AI-STS. Unlike traditional technical models, sociotechnical approaches recognize that innovation emerges from the dynamic interaction of technical, social, and environmental subsystems, each shaped by cognitive, organizational, and cultural processes. The integration of AI into STS introduces distinctive uncertainties, including algorithmic opacity, data biases, shifting organizational structures, and unpredictable multilevel dynamics that produce unintended consequences. These uncertainties complicate both design and governance, requiring adaptive, participatory, and iterative processes that foreground stakeholder diversity, distributed cognition, and accountability. By situating AI within dynamic STS, this chapter emphasizes how micro-level psychological processes, meso-level group and institutional processes, and macro-level sociopolitical and cultural externalities shape technological adoption and adaptation, perceptions of trust, and varying transformative human–machine configurations. In doing so, it further positions the STS framework as essential for addressing the needs of HCAI under conditions of complexity that generate deep uncertainties.
Keywords
Sociotechnical systems (STS) · Artificial intelligence (AI) · Human-centered design · Human factors · Human-centered AI · Ecosystems · Stakeholders · Multi-boundary systems · Multilevel analysis · Operationalization
1 Introduction
Technology is a defining feature of humans. Material (e.g., hydraulics, lithics, and mechanics) and symbol systems (e.g., language, number, iconography, and music) have enabled humans to adapt to their environment, creating ecological niches throughout the globe. These innovations emerge from dynamic interactions among stakeholders within evolving physical, social, and informational ecosystems defined by social learning and social contagion. Innovation ecosystems incorporate a set of actors, activities, and artifacts, with relationships evolving to identify and solve problems in ways that provide an advantage to adopters. It is in this light that technologies are developed to help humans adapt to their environment. As people become more dependent on their technologies and other agents, a sociotechnical system (STS) emerges (Trist & Bamforth, 1951). Ensuring that technologies are aligned with human capabilities, proprieties, values, beliefs, needs, and how these considerations inform technological development are critical, e.g., as in human systems integration (HSI). Technology and social structures are thus deeply intertwined and mutually shaped. At the heart of STS is joint optimization that emphasizes the need to design the technical, social, and environmental subsystems together to achieve the best overall system performance. STS emerge through evolutionary practices; they recognize that humans have agency and are not passive in how they select, use, modify, and reject their tools. In STS, the cultural context is equally important, with developers, distributors, and adopters shifting the power dynamics and political structures with stakeholders (e.g., users, data subjects, and regulators) who are affected by algorithmic decision-making. The proliferation of artificial intelligence (AI) as a product of, and a tool for, innovation poses unique challenges. AI as a product or process has the potential to supplement, replace, or augment humans, or digitally transform processes involving or impacting humans. When one considers introducing AI into a STS, we must address how joint optimization occurs in practice when agents are no longer human. Stakeholders are confronted with a myriad of questions. What social dynamics are created, altered, and disrupted within the STS when AI replaces the human users? What is the role of human-centered design (HCD) when people no longer have meaningful human control, and the system is fully automated or even autonomous? Where do humans fit into such a scenario and how might we ensure that they maintain a central position in the design and implementation processes?
1.1 Centering the Design Process on Humans
As AI is a technological artifact created by humans to support human activities and flourishing, human motives, goals, capacities, and social arrangements must remain central in its design. Hierarchical human-centered AI (hHCAI) provides a framework that acknowledges the complexity of AI-integrated sociotechnical systems (AI-STS) (Xu & Gao, 2025). While grounded in human factors and HCD, hHCAI extends beyond these traditions by explicitly addressing features distinctive to AI, such as scale, autonomy, and sociopolitical and cultural entanglement. hHCAI emphasizes not only the integration of human needs, values, and cognitive capabilities and capacities into AI design but also the importance of broader systemic considerations, including cultural diversity, institutional accountability, and ethical constraints.
Unlike many other technologies, the operations of AI remain largely opaque to users, regulators, and the public. Proprietary black box algorithms and massive data sets sometimes containing billions of parameters create opportunities for innovation but also pose significant threats to privacy, security, and moral and legal accountability. This perceived complexity often leads people to treat AI as a rarefied or inaccessible domain. Yet, AI systems do not operate in isolation. They are embedded within information and natural ecosystems, political and economic landscapes, and community and organizational structures. STSs themselves include environmental dimensions, creating potentially competing global AI innovation environments, such as China and the United States. Evaluating efficiency and efficacy, implementing and optimizing for specific use cases, forecasting systemic impacts, and regulating AI at national and international levels all require a systems-based approach.
Viewing AI as a collection of STSs nested within a broader ecosystem highlights the dynamic nature of these systems that function across multiple levels, from individual users and small groups to organizations, societies, and even species. AI systems comprise far more than algorithms and data; they include human users, end users, and usees (Baumer, 2015), as well as an array of stakeholders who enable the delivery of AI innovations, such as data annotators, design architects, and product managers. Yet, the properties of STSs are often underspecified at the level of individual agents and their interactions, the psychological level, while attention tends to focus instead on higher-level sociological forces. Discussions commonly emphasize value alignment and socially negotiated benefits for communities. Far less frequently do they address: (1) the psychological dispositions of individual designers, including their cognitive biases and entrenched mental models; (2) the psychology of users, which shapes system behaviors and is encoded in interaction patterns; (3) organizational psychology, particularly in contexts where technologies are deployed prematurely or corners are cut; and (4) collective psychological phenomena from large-scale STSs, such as near-constant connectivity, and the psychological effects of algorithmic decision-making on human agency.
Beyond the first phase of AI innovation rooted in computer science and psychology, contemporary approaches and regulatory efforts increasingly recognize that interdisciplinary collaboration is essential. No single field can adequately account for the features that define AI operations, how humans perceive the technical, social, environmental, and ethical affordances of these systems, how they consume and produce resources, or how the opportunities for action emerge from the interplay between system properties and human capabilities. Although contributions from the natural sciences remain indispensable, AI is ultimately created to benefit human agents. Existential debates aside regarding present or future machine agency, humans remain the only entities capable of responsible, autonomous action. Consequently, we must adopt a human-centered approach to design, one that understands “human” in the broadest sense, encompassing all stakeholders affected by the impacts of AI on human groups. It is humans, ultimately, who shape every dimension of the sociotechnical design process of an AI within the broader innovation ecosystem.
1.2 Overview
This chapter presents a HCD approach to AI, informed by sociotechnical theory and human factors within complex systems, in order to meet the requirements of humancentered AI (HCAI) (Xu, 2019; Shneiderman, 2022; Xu & Gao, 2025). We begin from the premise that technology, humans, and the environments in which people live and work are inseparable: technologies succeed only insofar as people perceive, understand, and use them to achieve desired outcomes within specific contexts governed by rules and regulations. While technologies are often shaped by their users, the relationship becomes more complex and asymmetrical in the context of AI due to several factors: (1) the opacity of AI systems, which constrains user agency; (2) the indirect influence of users, as AI systems are shaped primarily by available datasets and their validity; (3) the reduced responsiveness of AI to traditional usershaping mechanisms; (4) the potential distortion introduced by algorithmic mediation; and (5) collective action problems, where individual users lack the power to meaningfully shape AI systems given their scale and design. These challenges do not negate the value of a human-centered approach to sociotechnical design of AI; rather, they underscore the need to broaden participation beyond traditional users within HCD methodologies. The chapter begins with an overview of the evolution of sociotechnical theory, outlining its foundational principles and core characteristics. Section 2 examines STS in the context of AI and discusses how contemporary systems can augment or fundamentally transform existing practices, with varying degrees of human oversight, ranging from semiautonomous to fully autonomous or even agentic operations. Section 3 presents HCD as a critical approach to STS design and development, contrasting it with linear, traditional waterfall models. Here, emphasis is placed not only on technology but also on the human, and the broader environment in which people live and work. This section also considers the shift from viewing systems as closed or strictly intraorganizational to recognizing interorganizational and extraorganizational relationships. Such multi-boundary perspectives enable a holistic understanding of the innovation ecosystem, beyond a narrow focus on internal users. Human factors are also explored as part of the social subsystem of STSs, highlighting their influence on technical design decisions and system performance.
Section 4 introduces participatory design as an innovative practice, outlining varying degrees of stakeholder involvement, from basic engagement and consultation to fully collaborative design processes. This section also identifies organizational barriers to participatory approaches and the challenges of inclusive design, particularly for marginalized stakeholders who might be overlooked due to economic constraints or structural invisibility. Section 5 introduces analytic frameworks that apply a multilevel perspective to sociotechnical design, while Sect. 6 presents key HCD cognitive principles for STSs and mini-case examples that illustrate the practical value of applying sociotechnical theory to AI. In Sect. 7, future directions for the development and application of AI-STS are described, and in Sect. 8, the chapter concludes emphasizing the requirement for meaningful human control over AI participatory, adaptive design and governance approaches that are informed by cognitive limitations and the demands of distributed situational awareness.
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Citation: Jordan Richard Schoenherr, Katina Michael, Multi-Level Sociotechnical Systems Approach to Human-Centered AI, ed. Wei Xu, Handbook of Human-Centered Artificial Intelligence, Springer Nature Singapore, pp. 119-192.