Abstract Explainable Artificial Intelligence (XAI) is an emerging research field bringing
transparency to highly complex and opaque machine learning (ML) models. Despite the …

Deep neural networks have demonstrated excellent performances in many real-world
applications. Unfortunately, they may show Clever Hans-like behaviour (making use of …

This survey presents an overview of integrating prior knowledge into machine learning
systems in order to improve explainability. The complexity of machine learning models has …

The wide adoption of Machine Learning (ML) technologies has created a growing demand
for people who can train ML models. Some advocated the term" machine teacher''to refer to …

Although interactive learning puts the user into the loop, the learner remains mostly a black
box for the user. Understanding the reasons behind predictions and queries is important …

Reinforcement learning from human feedback (RLHF) is a variant of reinforcement learning
(RL) that learns from human feedback instead of relying on an engineered reward function …

Vicarious learning—individual learning that occurs through being exposed to and making
meaning from another's experience—has long been recognized as a driver of individual …

This paper presents a personalized gait optimization framework for lower-body
exoskeletons. Rather than optimizing numerical objectives such as the mechanical cost of …

When a robot performs a task next to a human, physical interaction is inevitable: the human
might push, pull, twist, or guide the robot. The state of the art treats these interactions as …

While constraints are ubiquitous in artificial intelligence and constraints are also commonly
used in machine learning and data mining, the problem of learning constraints from …