Autonomous AI shaft excavator: a case study on AI fairness for sustainability and green technology

In Japan, the number of skilled shaft excavator engineers is decreasing. To complete the Linear Central Shinkansen line of 286 km between Tokyo and Nagoya, an AI-equipped shaft excavator was prototyped to absorb the tacit knowledge of highly skilled engineers. The AI can predict penetration resistance and optimally controlling the excavator. This not only reduces drilling time, enhancing sustainability, but also cuts CO 2 emissions by half. Datasets are built based on standard penetration tests and an ensemble–ensemble method with 16 determinants is used, achieving a prediction accuracy of 0.9. This paper presents a case study that AI capabilities are there to fill the gap, extend the skills or meet the shortage in the labor market. Trust of AI in fairness is addressed by calculating fairness as a benchmark with a variety of fairness metrics from all disciplines. From an information management perspective, this paper explores methods for managing the tacit knowledge of highly skilled, diminishing workers in civil engineering to enhance the sustainability of services and products. Tacit knowledge can drive innovation to boost sustainability.