Backward Alignment is an indispensable part of the AI Alignment process, and the alignment issues from the Socio-Technical Systems (STS) perspective are an important component of it. For the current AI alignment field, "Socio-Technical" seems to have become an active keyword, appearing more or less in many recent works on AI safety and alignment. So, what exactly are STS, and what do they signify? In fact, STS is a very broad concept with many considerations, but so far, there has been little work that clearly unifies these issues in one go, often being glossed over in various materials. Additionally, different articles discuss STS using this grand term at different scales, or use different terms to define it at the same scale, which also makes it difficult for researchers to understand the field. This article will, from my personal perspective, clearly explain the AI alignment issues present in STS from a computable perspective at different scales, as well as possible research approaches.

What is a socio-technical system?

STS is a complex organizational work design method that recognizes the interaction between people and technology in real environments, and it requires consideration of the impact of technology deployment on the complex organizational structures of the original environment. It sounds complicated, but we can actually go back to the origin of the issue to understand it better. The term STS can be traced back to a case study by Eric Trist and Ken Bamforth in 1951, where the authors observed that improvements in coal mining technology did not lead to an increase in coal mine output. They further analyzed this case and found that the enhancement of technology caused dynamic changes in the original mining management system (such as increased worker absenteeism), which led to a new perspective based on the open system theory: in the process of developing and deploying technical subsystems, "people" are not dispensable; they form complex social subsystems across time and space based on different interests and goals. The involvement of technical subsystems requires dynamic consideration of their impact on social subsystems, including social and economic factors.

Complex AI systems are themselves STS, as each production component, including datasets, training, deployment, and usage, involves the intervention of different human participants. The manageme...

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