Recommender systems are software mechanisms whose usage is to offer suggestions for
different types of entities like products, services, or contacts that could be useful or …

This paper proposes a research direction to advance AI which draws inspiration from
cognitive theories of human decision making. The premise is that if we gain insights about …

Autonomous cyber-physical agents play an increasingly large role in our lives. To ensure
that they behave in ways aligned with the values of society, we must develop techniques that …

Machine ethics is the field that studies how ethical behaviour can be accomplished by
autonomous systems. While there exist some systematic reviews aiming to consolidate the …

Algorithms and machine learning models, including the decisions made by these models,
are becoming ubiquitous in our daily life, including hiring. We make no value judgment …

We study a constrained contextual linear bandit setting, where the goal of the agent is to
produce a sequence of policies, whose expected cumulative reward over the course of …

The more AI agents are deployed in scenarios with possibly unexpected situations, the more
they need to be flexible, adaptive, and creative in achieving the goal we have given them …

AI systems that learn through reward feedback about the actions they take are increasingly
deployed in domains that have significant impact on our daily life. However, in many cases …

Autonomous cyber-physical agents and systems play an increasingly large role in our lives.
To ensure that agents behave in ways aligned with the values of the societies in which they …

Contextual bandit techniques have recently been used for generating personalized user
recommendations in situations where collaborative filtering based algorithms may be …