2024/11/26 by Mario Truss, Marc Schmitt
Computer Science · Engineering · Social Sciences · #Digital Transformation in Industry #Ethics and Social Impacts of AI #Persona Design and Applications
paper · doi:10.1080/10447318.2024.2425454
openalex publication_date 2024/11/26 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/30
This paper addresses AI product prototyping, focusing on the challenges posed by the probabilistic nature of AI behavior and the limited accessibility of prototyping tools to AI non-experts. A design science research (DSR) approach is presented, which culminates in a conceptual framework for structuring the AI prototyping process with no-code AutoML technologies for textual and tabular ML use cases. Through a comprehensive literature review, key challenges were identified, and no-code AutoML was positioned as a solution. The framework describes the incorporation of non-expert input and evaluation during prototyping, leveraging the potential of no-code AutoML to enhance accessibility and interpretability. A hybrid approach combining naturalistic (case study) and artificial evaluation methods (criteria-based analysis) validated the utility of our approach, highlighting its efficacy in supporting AI non-experts and streamlining decision-making and its limitations. The implications for academia and industry focus on the strategic integration of no-code AutoML to enhance AI product development processes, mitigate risks, and foster innovation.