Refine
Year of publication
Document Type
- Article (5464) (remove)
Language
Has Fulltext
- no (5464) (remove)
Keywords
- avalanche (5)
- Earthquake (4)
- LAPS (4)
- field-effect sensor (4)
- frequency mixing magnetic detection (4)
- CellDrum (3)
- Heparin (3)
- capacitive field-effect sensor (3)
- hydrogen peroxide (3)
- magnetic nanoparticles (3)
- snow (3)
- tobacco mosaic virus (TMV) (3)
- Bacillus atrophaeus (2)
- Chemometrics (2)
- Datenschutz (2)
- Datenschutzgrundverordnung (2)
- Drinfeld modules (2)
- Empirical process (2)
- Field-effect sensor (2)
- Goodness-of-fit test (2)
Institute
- Fachbereich Medizintechnik und Technomathematik (1531)
- Fachbereich Wirtschaftswissenschaften (683)
- Fachbereich Elektrotechnik und Informationstechnik (617)
- Fachbereich Energietechnik (597)
- Fachbereich Chemie und Biotechnologie (585)
- INB - Institut für Nano- und Biotechnologien (523)
- Fachbereich Maschinenbau und Mechatronik (463)
- IfB - Institut für Bioengineering (426)
- Fachbereich Luft- und Raumfahrttechnik (364)
- Fachbereich Bauingenieurwesen (324)
- Solar-Institut Jülich (105)
- Fachbereich Architektur (76)
- Fachbereich Gestaltung (55)
- ZHQ - Bereich Hochschuldidaktik und Evaluation (39)
- ECSM European Center for Sustainable Mobility (33)
- Nowum-Energy (28)
- Sonstiges (23)
- Institut fuer Angewandte Polymerchemie (20)
- Freshman Institute (18)
- MASKOR Institut für Mobile Autonome Systeme und Kognitive Robotik (15)
Fledermäuse orientieren sich extrem schnell auch in absoluter Dunkelheit. Sie verwenden dazu eine Art Ultraschall Radar, um die Umgebung räumlich wahrzunehmen. Der Photo-Misch-Detektor (engl. Photonic Mixer Device, PMD) verwendet dieses Prinzip der Fledermäuse im optischen Bereich — dreidimensionale Szenen werden ohne Rechenaufwand kostengünstig, effizient und mit hoher Geschwindigkeit erfasst.
Kalkulation
(2006)
K0 production in e+e− annihilations at 30 GeV center of mass energy. TASSO Collaboration
(1980)
Providing healthcare services frequently involves cognitively demanding tasks, including diagnoses and analyses as well as complex decisions about treatments and therapy. From a global perspective, ethically significant inequalities exist between regions where the expert knowledge required for these tasks is scarce or abundant. One possible strategy to diminish such inequalities and increase healthcare opportunities in expert-scarce settings is to provide healthcare solutions involving digital technologies that do not necessarily require the presence of a human expert, e.g., in the form of artificial intelligent decision-support systems (AI-DSS). Such algorithmic decision-making, however, is mostly developed in resource- and expert-abundant settings to support healthcare experts in their work. As a practical consequence, the normative standards and requirements for such algorithmic decision-making in healthcare require the technology to be at least as explainable as the decisions made by the experts themselves. The goal of providing healthcare in settings where resources and expertise are scarce might come with a normative pull to lower the normative standards of using digital technologies in order to provide at least some healthcare in the first place. We scrutinize this tendency to lower standards in particular settings from a normative perspective, distinguish between different types of absolute and relative, local and global standards of explainability, and conclude by defending an ambitious and practicable standard of local relative explainability.