Digital Shadows and Twins for Human Experts and Data-Driven Services in a Framework of Democratic AI-based Decision Support

Open Access
Article
Conference Proceedings
Authors: Lucas PalettaHerwig ZeinerMichael SchneebergerYusuf Quadri

Abstract: Current automated and hierarchical structured production processes can only insufficiently deal with the upcoming flexibilization, specifically regarding the requirements within Industry 5.0. The European project FAIRWork fosters the ‘democratization’ of decision-making in production processes, hence the participation of all involved stakeholders, by introducing a decentralized AI system. Hybrid decision-making faces the challenge first to digitally represent the relevant actors – here we propose the use of digital twins – and the interpretation of that digital twin, by a human expert or by a computer algorithm, to achieve better decisions. Research on existing sensors and data technology is required. In particular, the digital representation of human operators requires so called “Intelligent Sensor Boxes”.Method: ‘Intelligent Sensor Boxes’ are firstly determined by a dedicated group of sensors, such as, low-cost sensors, biosensors, wearables, human sensors, or even virtual sensors. Specific attention is dedicated to the development of the ‘Digital Human Sensor’ (DHS) applying AI-enabled Human Factors measurement technology. Each instantiation of a DHS provides a digital vector of Human Factors state estimates, such as, digital biomarkers on physiological strain, affective state, concentration, cognitive workload, situation awareness, fatigue. On the basis of these vectors, we determine cost function parameters associated with typical (inter-)actions in the work environment. We outline an advanced approach to represent cognitive strain by studying workload related to task switching, multitasking and interruption as well as monotony effects. Furthermore, we will investigate cognitive strain in the context of environmental parameters, such as, air quality, and combine IOT with wearable bio-sensor shirts, smartwatches with biosensors, eye tracking glasses, digital events, and spatiotemporal patterns from human-machine interaction. Cost functions for optimization algorithms can be related to well-being of the worker, this allows data processing with the goal to optimize according to several input factors using data that are derived from humans or from machines like lines and robots. Those data are described with corresponding meta data to result in a descriptive data lake. Such meta data correspond to domain specific models like the production process, the working environment model, or resources models. Data processing and optimization algorithms can then be applied on this data lake. This task complements existing data with human based sensor data and provided adapted data mining tools.Results: We present relevant methodologies for human-centered wearable or mobile measurement technologies for psychological and ecological constructs as typical instantiations of the novel framework. Furthermore, the embedding of the schema of ‘Intelligent Sensor Boxes’ into the framework of ‘Democratic AI-based Decision Support’ (DAI-DDS) is sketched and argued. An outlook on future research trajectories, in particular, in the context of the FAIRWork project, is outlined in detail, and Ethical guidelines are discussed. Experimentation Laboratories, such as, the Austrian Human Factors Lab, are not only considered and presented as a co-creation space to develop new ideas, but also as test, training and communication environment for and between all stakeholders of an innovation chain. Conclusion: The framework and development of ‘Intelligent Sensor Boxes’ with data quality control and including decision modules is described in the context of its relevance within production related environments. ‘Digital Human Sensors’ applying AI-enabled digital Human Factors measurement technology will represent key drivers in the novel Industry 5.0 era.

Keywords: Democratic AI, Digital Shadow, Industry 5.0, Cognitive AI, AI, Humans

DOI: 10.54941/ahfe1003971

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