Titlebook: Receptors in the Developing Nervous System; Volume 2 Neurotransm Ian S. Zagon,Patricia J. McLaughlin Book 1993 Springer Science+Business Me

開始發(fā)作

有發(fā)明天才

不幸的人

Paul H. Robinson,John D. Stephenson,Timothy H. Moranfferent conditions, manipulating the transparency in a team. The results showed an interaction effect between the agents’ strategy and transparency on trust, group identification and human-likeness. Our results suggest that transparency has a positive effect in terms of people’s perception of trust,

Flat-Feet

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SHOCK

Rebecca M. Prussng exploitation of ML-based approaches generated opaque systems, which are nowadays no longer socially acceptable—calling for eXplainable AI (XAI). Such a problem is exacerbated when IS tend to approach safety-critical scenarios. This paper highlights the need for on-time explainability. In particul

Pander

F. Javier Garcia-Ladona,Guadalupe Mengod,José M. Palacios agents using two different algorithms which automatically generate different explanations for agent actions. Quantitative analysis of three user groups (n?=?20, 25, 20) in which users detect the bias in agents’ decisions for each explanation type for 15 test data cases is conducted for three differ

Cpap155

Sandra E. Loughlin,Frances M. Leslie agents using two different algorithms which automatically generate different explanations for agent actions. Quantitative analysis of three user groups (n?=?20, 25, 20) in which users detect the bias in agents’ decisions for each explanation type for 15 test data cases is conducted for three differ

對(duì)手

Edythe D. London,Stephen R. Zukin agents using two different algorithms which automatically generate different explanations for agent actions. Quantitative analysis of three user groups (n?=?20, 25, 20) in which users detect the bias in agents’ decisions for each explanation type for 15 test data cases is conducted for three differ

AMITY

Ann Tempelng exploitation of ML-based approaches generated opaque systems, which are nowadays no longer socially acceptable—calling for eXplainable AI (XAI). Such a problem is exacerbated when IS tend to approach safety-critical scenarios. This paper highlights the need for on-time explainability. In particul