Explainable AI (XAI) has been investigated for decades and, together with AI itself, has
witnessed unprecedented growth in recent years. Among various approaches to XAI …

AI has become pervasive in recent years, but state-of-the-art approaches predominantly
neglect the need for AI systems to be contestable. Instead, contestability is advocated by AI …

We introduce a conceptualisation for generating argumentation frameworks (AFs) from
causal models for the purpose of forging explanations for models' outputs. The …

Causal models are playing an increasingly important role in machine learning, particularly in
the realm of explainable AI. We introduce a conceptualisation for generating argumentation …

Truth discovery networks evaluate the trustworthiness of sources (eg, websites) and their
claims (eg, the severity of a virus). Intuitively, the more trustworthy the sources of a claim, the …

We introduce a conceptualisation for generating argumentation frameworks (AFs) from
causal models for the purpose of forging explanations for models' outputs. The …

In this paper we show how re-interpreting PageRank as an argumentation semantics for a
bipolar argumentation framework empowers its explainability. After showing that PageRank …

Quantitative bipolar argumentation frameworks (QBAFs) have various applications in areas
like product recommendation, review aggregation and explaining machine learning models …

In this paper we show how re-interpreting PageRank as an argumentation semantics for a
bipolar argumentation framework empowers its explainability. To this purpose we propose …

Classroom discussions are important for enriching the student's background knowledge and
improving the student's logical thinking ability. Automatic evaluation of argument strengths in …