The process guideline developed within the HUMAINE project is the product of multidisciplinary and interdisciplinary research and builds on the diverse expertise and methodological approaches of the scientific partners involved. he scientific knowledge building process that results in the process blueprint progresses through ongoing testing with our application partners and is enlivened by feedback with other researchers and practitioners. Therefore, we provide an excerpt of our very different tools on this page, which are applied within the HUMAINE project. The common goal of all instruments is the scientific support of human-centered AI applications in operational practice. Please note that the content of the Toolbox is currently only available in German. If you have a precise need for an English translation, please do not hesitate to contact us!
Instruments for Strategy and Business Model Development
When organizations look at artificial intelligence in their work processes, it implies business decisions and strategic planning. The tools listed here represent support services in this field, taking into account a human-centered approach to AI.
Determine whether it is profitable for a company to further pursue a human-centric AI approach in the future to position itself competitively (e.g., business unit development, customer acquisition and retention, employer attractiveness). Identifying necessary course settings for this.
Moderated workshop concept with feedback loops and participation offer to stakeholders / interest groups
Authors: Prof. Dr. Uta Wilkens, Dr. Valentin Langholf
Contact: Prof. Dr. Uta Wilkens (uta.wilkens@rub.de)
The VBMC is used to visualize and structure value-oriented AI-based business models. This allows the current business model of a company to be mapped, so that starting points for the use of AI become clear. It can be equally useful in the value-based development of new AI-based business model. Questions are associated with each building block, providing question-based guidance for modeling a value-based AI-based business model. In this way, users can become clear about their strategic orientation.
Instruments for Work and Work System Design
Human-centric AI use is also expressed by the fact that technology, organizational, and employee development are all given equal consideration in the further development of work systems. The tools listed here provide starting points for considering changing workflows, changing work roles, and stimulating and personality-enhancing workplaces when designing human-AI work systems.
Exploratory qualitative research
Contact: Prof. Dr. Uta Wilkens (uta.wilkens@rub.de)
Basic understanding of processes, company and context. Anticipated challenges for AI integrations. Required spectrum of analysis.
Identify identity-, well-being-, and motivation-enhancing workplace characteristics as cues for human-centered AI implementation and workplace design.
Standardized online survey
Contact: Sophie Berretta (sophie.berretta@ruhr-uni-bochum.de)
The aim is to impart knowledge and design practice to development and user companies in order to be able to verify the fulfillment of the criteria of human-centered work design (e.g., harmlessness, facilitation of learning, protection of personal rights, and job security) in AI implementation processes.The aim is to impart knowledge and design practice to development and user companies in order to be able to verify the fulfillment of the criteria of human-centered work design (e.g., harmlessness, facilitation of learning, protection of personal rights, and job security) in AI implementation processes.
Action research
Contact: Dr. Anja Gerlmaier (anja.gerlmaier@uni-due.de), Alexander Bendel (alexander.bendel@uni-due.de)
Instruments for Human-centered Technology Development
The specific design of technical systems can take people into account to varying degrees and in different ways. The tools listed here provide an approach to AI applications and AI systems that goes beyond pure technology development. Not only specific forms of AI modeling (active learning) but also support systems for learning processes (learning factories) will be considered.
Users should be able to see where humans can collaborate with machines in a comprehensive AI solution and what the benefits are.
tachAId is a process-accompanying, interactive guide, which not only informs, but also gives specific recommendations regarding the implementation or realization of different software and concepts based on the answer options.
Contact: Max Bauroth (max.bauroth@ini.rub.de), Pavlos Rath-Manakidis (pavlos.rath-manakidis@ini.rub.de)
Survey the goal and purpose of future learning scenario developments.
The document provides an overview of issues that may arise when using Active Learning.
Authors: Jan Freiwald, Dorothea Kolossa und weitere
