Currently, explainability represents a major barrier that Artificial Intelligence (AI) is facing in
regard to its practical implementation in various application domains. To combat the lack of …

In recent years, artificial intelligence (AI) technology has been used in most if not all domains
and has greatly benefited our lives. While AI can accurately extract critical features and …

The rapid growth and use of artificial intelligence (AI)-based systems have raised concerns
regarding explainability. Recent studies have discussed the emerging demand for …

Legal syllogism is a form of deductive reasoning commonly used by legal professionals to
analyze cases. In this paper, we propose legal syllogism prompting (LoT), a simple …

Many legal professionals think the explosion of information about local, regional, national,
and international legislation makes their practice more costly, time-consuming, and error …

Researchers have developed a variety of approaches to evaluate explainable artificial
intelligence (XAI) systems using human–computer interaction (HCI) user‐centered …

The feature attribution-based explanation (FAE) methods, which indicate how much each
input feature contributes to the model's output for a given data point, are one of the most …

Automatic legal text classification systems have been proposed in the literature to address
knowledge extraction from judgments and detect their aspects. However, most of these …

The factors complicating the specification of requirements for artificial intelligence systems
(AIS) and their verification for the AIS creation and modernization are analyzed. The …

We use recent explainable artificial intelligence (XAI) based on SHapley Additive
exPlanations (SHAP), Local Interpretable Model-agnostic Explanations (LIME), and Light …