"Title: Explainable Artificial Intelligence in Generative Design for Construction
Authors: Zarghami, Sanaz (1);
Kouchaki, Hanieh (2);
Yang, Longzhi (1);
Martinez Rodriguez, Pablo (1)
Affiliation: 1: Northumbria University, United Kingdom;
2: Tabriz Islamic Art University, Iran
Keywords: explainable artificial intelligence; generative design; trustworthiness; construction; artificial intelligence
Session: Data Sensing & Acquisition
Paper Link: 10.35490/EC3.2024.277
Abstract: As artificial intelligence rapidly advance, their growing complexity enables more sophisticated applications across sectors, including construction. However, the opaque nature of algorithms, such as generative AI, reduces human interpretability and trust. While providing benefits like enhanced efficiency, generative design’s black-box processes hamper adoption. Explainable AI can elucidate how AI algorithms generate outputs, thereby improving understanding and confidence. Despite explainable AI’s potential, construction has given it limited focus. This research systematically reviews the application of explainable AI in generative design in construction, with an aim to allay risks and enable wider utilization of these emerging technologies for improved engineering design."
