Abstract Recently, the eXplainable AI (XAI) research community has focused on developing
methods making Machine Learning (ML) predictors more interpretable and explainable …

An Abstract Framework for Agent-Based Explanations in AI Page 1 An Abstract Framework for
Agent-Based Explanations in AI Extended Abstract Giovanni Ciatto University of Bologna …

Because of recent and rapid developments in Artificial Intelligence (AI), humans and AI-
systems increasingly work together in human-agent teams. However, in order to effectively …

Explainable AI (XAI) has emerged in recent years as a set of techniques and methodologies
to interpret and explain machine learning (ML) predictors. To date, many initiatives have …

Explainability has been a core research topic in AI for decades and therefore it is surprising
that the current concept of Explainable AI (XAI) seems to have been launched as late as …

This paper presents objective metrics for how explainable artificial intelligence (XAI) can be
quantified. Through an overview of current trends, we show that many explanations are …

During the last few years, the topic of explainable artificial intelligence (XAI) has become a
hotspot in the ML research community. Model-agnostic interpretation methods propose …

In the last few years, Artificial Intelligence (AI) has achieved a notable momentum that, if
harnessed appropriately, may deliver the best of expectations over many application sectors …

Thanks to the exponential growth in computing power and vast amounts of data, artificial
intelligence (AI) has witnessed remarkable developments in recent years, enabling it to be …

In this paper I argue that the search for explainable models and interpretable decisions in AI
must be reformulated in terms of the broader project of offering a pragmatic and naturalistic …