Predictions obtained by, eg, artificial neural networks have a high accuracy but humans
often perceive the models as black boxes. Insights about the decision making are mostly …

Explainable Artificial Intelligence (XAI) has experienced a significant growth over the last few
years. This is due to the widespread application of machine learning, particularly deep …

At the dawn of the fourth industrial revolution, we are witnessing a fast and widespread
adoption of artificial intelligence (AI) in our daily life, which contributes to accelerating the …

In recent years, many accurate decision support systems have been constructed as black
boxes, that is as systems that hide their internal logic to the user. This lack of explanation …

The recent years have witnessed the rise of accurate but obscure decision systems which
hide the logic of their internal decision processes to the users. The lack of explanations for …

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 …

The group testing problem concerns discovering a small number of defective items within a
large population by performing tests on pools of items. A test is positive if the pool contains …

Black box AI systems for automated decision making, often based on machine learning over
(big) data, map a user's features into a class or a score without exposing the reasons why …

Deep neural networks have been proven powerful at processing perceptual data, such as
images and audio. However for tabular data, tree-based models are more popular. A nice …

We develop an economic perspective on algorithmic fairness. Algorithmic bias and fairness
issues are appearing in an increasing variety of economic research literatures. Our …