Predictive maintenance is a well studied collection of techniques that aims to prolong the life
of a mechanical system by using artificial intelligence and machine learning to predict the …

In recent years, black-box machine learning approaches have become a dominant modeling
paradigm for knowledge extraction in Remote Sensing. Despite the potential benefits of …

Explainable artificial intelligence (XAI) has been increasingly investigated to enhance the
transparency of black-box artificial intelligence models, promoting better user understanding …

The literature addressing bias and fairness in AI models (fair-AI) is growing at a fast pace,
making it difficult for novel researchers and practitioners to have a bird's-eye view picture of …

While existing literature has explored and revealed several insights pertaining to the role of
human factors (eg, prior experience, domain knowledge) and attributes of AI systems (eg …

For large language models (LLMs) to be trusted by humans they need to be well-calibrated
in the sense that they can accurately assess and communicate how likely it is that their …

This study explored the potential of eXplainable Artificial Intelligence (XAI) in raising user
awareness of algorithmic bias. This study examined the popular “explanation by example” …

Nowadays, our security and privacy are strongly threatened by malware programs which
aim to steal our confidential data and make our systems out of service, among other things …

Decision processes of computer vision models—especially deep neural networks—are
opaque in nature, meaning that these decisions cannot be understood by humans. Thus …

With the rise of artificial intelligence (AI), concerns about AI applications causing unforeseen
harms to safety, privacy, security, and fairness are intensifying. While attempts to create …