The difficulty of explaining the outputs of artificial intelligence (AI) models and what has led
to them is a notorious ethical problem wherever these technologies are applied, including in …

The proposed framework named IDEAL (Interpretable-by-design DEep learning ALgorithms)
recasts the standard supervised classification problem into a function of similarity to a set of …

In a continent marked by its historical pursuit of secure borders, Africa now stands at a
pivotal juncture, transitioning from traditional physical barriers to harnessing the …

Issues of reliability are claiming center‐stage in the epistemology of machine learning. This
paper unifies different branches in the literature and points to promising research directions …

It is commonly accepted that clinicians are ethically obligated to disclose their use of medical
machine learning systems to patients, and that failure to do so would amount to a moral fault …

Abstract The Sammen Om Demens (together for dementia), a citizen science project
developing and implementing an AI-based smartphone app targeting citizens with dementia …

Extant literature points to how the risk of discrimination is intrinsic to AI systems owing to the
dependence on training data and the difficulty of post hoc algorithmic auditing …

This paper develops an account of how the implementation of ML models into healthcare
settings requires revising the methodological apparatus of philosophical bioethics. On this …

With the emergence of renewable energy sources, designing tariffs becomes crucial to
match unstable energy supply with varying energy demand. However, traditional tariff …

AI-enabled devices are increasingly introduced in the home context and cyber-attacks
targeting their AI component are becoming more frequent. Moving away from seeing the …