The ethics of artificial intelligence in healthcare: From hands-on care to policy-making.

Contemporary healthcare at all levels increasingly uses Artificial Intelligence (AI). However, since the various levels involve different tasks, have different data needs, and different ethical obligations, the AIs that are used have to be differently structured. Also, since healthcare construed as a commodity involves different ethical parameters from healthcare construed as a right, and different ethical systems entail logically distinct considerations, this also necessitates the need for differently structured AIs.

Big Data, Information Technology and Information Professionals: Some Considerations for Digital Ethics.

It is generally accepted that the global evolution of health information technology, both in design and usage, raises ethical issues that should be addressed. However, the terms in which this concern is expressed are shrouded in ambiguity. Even what constitutes an ethical issue itself is never clearly defined.

Artificial intelligence in healthcare: Ethical considerations.

The term Artificial Intelligence (AI) is systematically ambiguous between electronic expert systems that are used by healthcare professionals in carrying out their tasks and full AIs, which are stand-alone independent electronic entities that function much like human healthcare professionals except that they are electronic and not biological in nature. This discussion sketches the distinct ethical considerations that are relevant to the two kinds of AI while acknowledging that currently there are no full AIs.

Erratum to: Measurement of the boson polarisation in events from collisions at in the lepton + jets channel with ATLAS.

[This corrects the article DOI: 10.1140/epjc/s10052-017-4819-4.].

Measurement of the Soft-Drop Jet Mass in pp Collisions at sqrt[s]=13 TeV with the ATLAS Detector.

Jet substructure observables have significantly extended the search program for physics beyond the standard model at the Large Hadron Collider. The state-of-the-art tools have been motivated by theoretical calculations, but there has never been a direct comparison between data and calculations of jet substructure observables that are accurate beyond leading-logarithm approximation. Such observables are significant not only for probing the collinear regime of QCD that is largely unexplored at a hadron collider, but also for improving the understanding of jet substructure properties that are used in many studies at the Large Hadron Collider.