AN INTEGRATED RECIPROCAL HUMAN AI SOCIO-TECHNICAL FRAMEWORK: ENHANCING CREATIVE SYNERGY IN CROWDSOURCED DESIGN
DOI:
https://doi.org/10.55197/qjssh.v6i4.797Keywords:
crowdsourced design, AI automation, human intuition, Reciprocal Human-Machine Learning (RHML), algorithm aversionAbstract
This concept paper discusses the Integrated Reciprocal Human-AI Socio-Technical Framework, which integrates artificial intelligence (AI) automation with human creativity in crowdsourced design settings. Although AI systems contribute to the efficiency of the design, their scalability, and automation of the tasks, their increasing monopoly poses the risk of rendering human intuition, creative autonomy, and ethical control unimportant. The framework suggested will be based on Reciprocal Human-Machine Learning (RHML), in which dynamic, two-way learning between human designers and AI systems will be facilitated. Two important mediating constructs, Polanyi Paradox and Algorithm Aversion, underline the incomparability of the tacit human knowledge and the significance of transparency and trust in the relations between humans and AI. Ethics can be considered as a balancing force, which guarantees equity, protection of intellectual property, and open business in the design process. All these elements are meant to create a creative synergy state, in which AI does not substitute the functions of human creativity but supplements them. The framework focuses on the key gaps in the current AI-based design systems because it focuses on the outcomes of the collaboration between humans and AI systems instead of the automation-based results. It is suggested that future studies empirically support the framework by using qualitative research that will involve creative professionals working on AI-supported platforms. The paper will add a sustainable, ethically-based roadmap of how to incorporate AI in collaborative creative work without sacrificing human uniqueness or agency.
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