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AI models and services are used in a growing number of highstakes areas, resulting in a need for increased transparency. Consistent with this, several proposals for higher quality and more consistent documentation of AI data, models, and systems have emerged. Little is known, however, about the needs of those who would produce or consume these new forms of documentation. Through semi-structured developer interviews, and two document creation exercises, we have assembled a clearer picture of these needs and the various challenges faced in creating accurate and useful AI documentation. Based on the observations from this work, supplemented by feedback received during multiple design explorations and stakeholder conversations, we make recommendations for easing the collection and flexible presentation of AI facts to promote transparency.
The Common Object Request Broker Architecture (CORBA) is successfully used in many control systems (CS) for data transfer and device modeling. Communication rates below 1 millisecond, high reliability, scalability, language independence and other fea
Algorithmic transparency entails exposing system properties to various stakeholders for purposes that include understanding, improving, and contesting predictions. Until now, most research into algorithmic transparency has predominantly focused on ex
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In aerospace and defense, training is being carried out on the web by viewing PowerPoint presentations, manuals and videos that are limited in their ability to convey information to the technician. Interactive training in the form of 3D is a more cos
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