'Hard AI Crime': The Deterrence Turn.

Machines powered by artificial intelligence (AI) are increasingly taking over tasks previously performed by humans alone. In accomplishing such tasks, they may intentionally commit 'AI crimes', ie engage in behaviour which would be considered a crime if it were accomplished by humans. For instance, an advanced AI trading agent may-despite its designer's best efforts-autonomously manipulate markets while lacking the properties for being held criminally responsible.

Argumentation and explanation in the law.

This article investigates the conceptual connection between argumentation and explanation in the law and provides a formal account of it. To do so, the methods used are conceptual analysis from legal theory and formal argumentation from AI. The contribution and results are twofold.

Assessing Trustworthy AI in Times of COVID-19: Deep Learning for Predicting a Multiregional Score Conveying the Degree of Lung Compromise in COVID-19 Patients.

This article's main contributions are twofold: 1) to demonstrate how to apply the general European Union's High-Level Expert Group's (EU HLEG) guidelines for trustworthy AI in practice for the domain of healthcare and 2) to investigate the research question of what does "trustworthy AI" mean at the time of the COVID-19 pandemic. To this end, we present the results of a post-hoc self-assessment to evaluate the trustworthiness of an AI system for predicting a multiregional score conveying the degree of lung compromise in COVID-19 patients, developed and verified by an interdisciplinary team with members from academia, public hospitals, and industry in time of pandemic. The AI system aims to help radiologists to estimate and communicate the severity of damage in a patient's lung from Chest X-rays.