Komplementäre Arbeitsgestaltung. Grundrisse eines Konzepts zur Humanisierung der Arbeit mit KI

Huchler, Norbert

doi:10.1007/s41449-022-00319-5

Cited by 14 publications

(19 citation statements)

References 26 publications

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“…If we consider connectionist AI as an empirical object of research on work, we can see that it is a technology that can be used and interpreted in very different ways in different fields and contexts (Brynjolfsson & McAfee, 2014;Ford, 2016). Broadly speaking, discussions on AI in the sociology of work focus on how it can replace human work (substitution), complement human work (complementarity) or empower working subjects (augmentation) (Huchler, 2019(Huchler, , 2022. This raises further questions concerning possible uses of AI in the world of work: 4 does AI accelerate automation by itself, or does it act as a tool?…”

Section: Artificial Intelligence As a Challenge For Sociology (Of Work)mentioning

confidence: 99%

“…With regard to work, AI can be understood as a new method of informatisation or computerisation of work and society (Baukrowitz et al, 2006), as automation (Huchler, 2022) or as a new driver of productivity, rationalisation, but also in relation to other trends such as globalisation, financialisation and networking (Schmiede, 2006). AI can serve to couple more tightly or more loosely the control of work and concrete work practices, as well as the formal and informal structures of organisations (Weltz, 1988).…”

Section: Artificial Intelligence As a Challenge For Sociology (Of Work)mentioning

confidence: 99%

“…Thus, AI can be seen not just as an information technology artifact, but also as a data processing method that even transcends the computer as a 'universal machine' (Schmiede, 2006:461). Therefore, the new expectations on AI (for a critical account see Brödner, 2019 andHeinlein &Huchler, 2022) are not only linked to its potential to represent relevant work processes in data ('digital twin') and make them calculable or objectifiable. Rather, the integration of AI into work processes means that complexity and uncertainty no longer have to be reduced to linear and formal digital processes based on expert knowledge.…”

Section: Artificial Intelligence: Enabling a Situational Approach To ...mentioning

confidence: 99%

“…In some cases, AI systems are based on very sophisticated and reliable learning algorithms and, at the same time, on very simplified impact models (Brödner, 2019). Informational modelling of work can easily fail (Rohde et al, 2017) especially because it is built on the formalisation of social practices (Schmiede, 2006;Brödner, 2019;Huchler 2022). For example, AI systems for personnel selection that promise to be able to draw conclusions about a person's suitability for certain jobs on the basis of elaborate written, voice or video analyses, can at the same time rely on simple models borrowed from psychology (such as 'red, green, blue people') 6 .…”

Section: Social Selectivities In the Embedding Of Aimentioning

confidence: 99%

“…This is further complicated by the fact that socio-technical systems are continuously changing. For example, new work is constantly being created around technical automation processes (Huchler 2022). Last but not least, there is the problem of explicating and objectifying knowledge regarding the limits of the technical translation of data into information and knowledge (Schmiede, 2006).…”

Section: Selectivities In the Mastering Of Social Complexity Through ...mentioning

confidence: 99%

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Artificial intelligence in the practice of work

Heinlein¹,

Huchler²

2023

WOLG

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The article proposes an analytical perspective on artificial intelligence (AI) that can be fruitful in the sociology of work. The practical logic of new forms of AI (connectionist AI) is described as an interplay of social and technical processes of opening and closing possibilities of knowledge and action. In order to develop this argument, it is first shown in which sense AI can be understood as a contingency-generating technology in socio-technical contexts. The architecture based on neural networks is elaborated as a decisive feature of connectionist AI that not only opens up technical possibilities but can also shape social processes and structures by ‘selectivity’. However, this shaping does not take place solely on the part of the AI, but only becomes apparent in the interplay with specific restrictions that lie both in the social context of use and in the algorithmic architecture of the AI itself. For research in the sociology of work, this means that contingency theory approaches must be linked with approaches that emphasise the limits of (‘intelligent’) digitalisation. The yield of such a perspective is outlined in relation to the control of work with AI.

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Section: Artificial Intelligence As a Challenge For Sociology (Of Work)mentioning

confidence: 99%

Section: Artificial Intelligence As a Challenge For Sociology (Of Work)mentioning

confidence: 99%

Section: Artificial Intelligence: Enabling a Situational Approach To ...mentioning

confidence: 99%

Section: Social Selectivities In the Embedding Of Aimentioning

confidence: 99%

Section: Selectivities In the Mastering Of Social Complexity Through ...mentioning

confidence: 99%

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Artificial intelligence in the practice of work

Heinlein¹,

Huchler²

2023

WOLG

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Transformation of Work in the Textile Industry: Perspectives of Sustainable Innovation Processes

Altepost,

Hansen-Ampah,

Merx

et al. 2024

Transformation Towards Sustainability

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What makes innovation processes in industry succeed? The basic assumption of this paper is that not only technological, but also social—especially work-related—factors have a decisive impact. While processes of sociotechnical system design are established interdisciplinarily and have arrived at least in many large companies, to the best of our knowledge it still is a novelty in industrial contexts to also add the concept of sustainability to this perspective. Energy and circular economy as well as a shortage of skilled workers dominate the concerns of companies. At the same time, technologies such as artificial intelligence (AI) are traded as a beacon of hope to strengthen competitiveness and contribute to more efficient, resource-conserving economic activity (e.g., Lukic et al., BCG 10.01.2023, 2023).). With the design of AI-supported work systems in the textile and related industries, the WIRKsam Competence Center for Work Research wants to show how the use of artificial intelligence, with appropriate work design, can promote both innovative, human-centered work and economic competitiveness, so that the two benefit from each other. The project aims to strengthen the industrial backbone of the Rhenish mining area and to create attractive conditions and opportunities for skilled workers. In this way, a sustainable result of the various transformation levels in the area of structural change, digitalization and the future of work can be achieved, which lays the foundation for shaping further future transformation processes in an innovative way. In this paper, we develop central questions originating from this claim that need to be considered in the aforementioned transformation processes in the areas of people, technology and organization, because they can be decisive for success.

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Selektivitäten (subsymbolischer) Künstlicher Intelligenz

Huchler

2024

Künstliche Intelligenz, Mensch Und Gesellschaft

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Komplementäre Arbeitsgestaltung. Grundrisse eines Konzepts zur Humanisierung der Arbeit mit KI

Cited by 14 publications

References 26 publications

Artificial intelligence in the practice of work

Artificial intelligence in the practice of work

Transformation of Work in the Textile Industry: Perspectives of Sustainable Innovation Processes

Selektivitäten (subsymbolischer) Künstlicher Intelligenz

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