On developers’ practices for hazard diagnosis in machine learning systems

Machine learning (ML) is an artificial intelligence technology that has a great potential for being adopted in various sectors of activities. Yet, it is now also increasingly recognized as a hazardous technology. Failures in the outputs of an ML system might cause physical or social harms. Besides, the development and deployment of an ML system...

Utilization of response surface methodology and machine learning for predicting and optimizing mixing and compaction temperatures of bio-modified asphalt

The optimization of energy consumption during asphalt mixture production and compaction is a challenge in producing durable, sustainable, and environmentally friendly asphalt products. This study investigated the effects of crude palm oil (CPO) and/or tire pyrolysis oil (TPO) on shear viscosity and mixing and compaction temperatures of...

Prediction & optimization of alkali-activated concrete based on the random forest machine learning algorithm

Alkali-activated concrete (AAC) is regarded as a promising alternative construction material to reduce the CO₂ emission induced by Portland cement (PC) concrete. Due to the diversity in raw materials and complexity of reaction mechanisms, a commonly applied design code is still absent to date. This study attempts to directly...

Explainable AI for Designers: A Human-Centered Perspective on Mixed-Initiative Co-Creation

Growing interest in eXplainable Artificial Intelligence (XAI) aims to make AI and machine learning more understandable to human users. However, most existing work focuses on new algorithms, and not on usability, practical interpretability and efficacy on real users. In this vision paper, we propose a new research area of eXplainable AI for...