Work

Machine Learning (ML) diffusion in the design process: A study of Norwegian design consultancies

Year: 2023

Type: article

Abstract: Traditionally, the design process has been performed by designers and developers with the aid of digital technologies. The proliferation of Machine Learning (ML) during the last years has been argued to boost the creative process of design. This includes simple tasks such as translating handwritten notes, suggesting layouts options but also more complex action possibilities like generation of new ideas and prototyping for their visualization. However, the discourse about ML in creative industries is in an early stage, and there is limited knowledge about its diffusion in the design process. In our case study of four Norwegian design consultancies, we found that inhibitors (lack of ML knowledge, lack of trust in ML outputs, and poor results provided in languages other than English) overweighted the enablers (identifying patterns in the transcriptions, checking the requirements). This limited the intentions of design consultancies to introduce ML and undermined its diffusion in their design process.

Source: Technological Forecasting and Social Change

DOI: 10.1016/j.techfore.2023.122724

Authors: Cristina Trocin, Åsne Stige, Patrick Mikalef

Institutions: Newcastle University, Norwegian University of Science and Technology, SINTEF, SINTEF Digital

Language: English

Topic: Design Education and Practice

Subfield: Mechanical Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: hybrid

Estimated APC paid: $2,500