An increasing demand for model explainability has accompanied the widespread adoption
of transformers in various fields of applications. In this paper, we conduct a survey of the …

Concept-based explanations work by mapping complex model computations to human-
understandable concepts. Evaluating such explanations is very difficult, as it includes not …

Interpreting and understanding the predictions made by deep learning models poses a
formidable challenge due to their inherently opaque nature. Many previous efforts aimed at …

Beyond improving trust and validating model fairness, xAI practices also have the potential
to recover valuable scientific insights in application domains where little to no prior human …

The problem of algorithmic bias in machine learning has recently gained a lot of attention
due to its potentially strong impact on our societies. In much the same manner, algorithmic …

The burgeoning field of Natural Language Processing (NLP) stands at a critical juncture
where the integration of fairness within its frameworks has become an imperative. This PhD …

The fairness of Natural Language Processing (NLP) models has emerged as a crucial
concern. Information theory indicates that to achieve fairness, a model should not be able to …

In the rapidly evolving and increasingly complex field of Artificial Intelligence (AI),
understanding and interpreting the decision‐making process of models is crucial. This …

Περίληψη Η τεχνητή νοημοσύνη υπέστη εκρηκτική εξέλιξη τα τελευταία χρόνια. Με κινητήριο
δύναμη την τεχνολογία της βαθιάς μάθησης, η τεχνητή νοημοσύνη βρίσκει εφαρμογή σε …

With integration of collaborative robotics, robotic vision, and machine learning, the high-end
frontier reached in assistive robotics is likely to help develop promising solutions to the …