Machine and deep learning have proven their utility to generate data-driven models with
high accuracy and precision. However, their non-linear, complex structures are often difficult …

During the COVID-19 crisis, customers' preference in having food delivered to their doorstep
instead of waiting in a restaurant has propelled the growth of food delivery services (FDSs) …

In recent years, there has been a rapidly expanding focus on explaining the predictions
made by black-box AI systems that handle image and tabular data. However, considerably …

Statistical methods were traditionally primarily used for time series forecasting. However,
new hybrid methods demonstrate competitive accuracy, leading to increased machine …

Recently, eXplainable AI (XAI) research has focused on the use of counterfactual
explanations to address interpretability, algorithmic recourse, and bias in AI system decision …

Participatory Budgeting (PB) has evolved into a key democratic instrument for resource
allocation in cities. Enabled by digital platforms, cities now have the opportunity to let …

Machine learning models are being increasingly deployed to take, or assist in taking,
complicated and high-impact decisions, from quasi-autonomous vehicles to clinical decision …

The generative adversarial network (GAN) is one of the most promising methods in the field
of unsupervised learning. Model developers, users, and other interested people are highly …

As deep learning models become increasingly complex, practitioners are relying more on
post hoc explanation methods to understand the decisions of black-box learners. However …

Abstract Explanatory Interactive Learning (XIL) collects user feedback on visual model
explanations to implement a Human-in-the-Loop (HITL) based interactive learning scenario …