Attribution and Artificial Intelligence: Embedding Provenance with Material Metadata
Date
Authors
Major Professor
Advisor
Committee Member
Journal Title
Journal ISSN
Volume Title
Publisher
Authors
Research Projects
Organizational Units
The Department offers a five-year program leading to the Bachelor of Architecture degree. The program provides opportunities for general education as well as preparation for professional practice and/or graduate study.
The Department of Architecture offers two graduate degrees in architecture: a three-year accredited professional degree (MArch) and a two-semester to three-semester research degree (MS in Arch). Double-degree programs are currently offered with the Department of Community and Regional Planning (MArch/MCRP) and the College of Business (MArch/MBA).
History
The Department of Architecture was established in 1914 as the Department of Structural Design in the College of Engineering. The name of the department was changed to the Department of Architectural Engineering in 1918. In 1945, the name was changed to the Department of Architecture and Architectural Engineering. In 1967, the name was changed to the Department of Architecture and formed part of the Design Center. In 1978, the department became part of the College of Design.
Dates of Existence
1914–present
Historical Names
- Department of Structural Design (1914–1918)
- Department of Architectural Engineering (1918–1945)
- Department of Architecture and Architectural Engineering (1945–1967)
Related Units
- College of Design (parent college)
- College of Engineering(previous college, 1914–1978)
Journal Issue
Is Version Of
Versions
Series
Department
Abstract
When artificial intelligence participates in design, the notion of attribution–and accompanying systems of rights, credit, royalties, etc.–is brought into question. Without some means of identifying and negotiating the use of the contributions of non-human authors, works produced by algorithmic systems and Als may lack the requirements to be recognized as works of authorship under international laws or in academic institutions. This deficiency could prohibit databases of digital models, algorithms, and toolpaths, for example, from being appropriately accessed by other Als to improve designs and create new ones. Machine learning is most efficient when it has not only access to data but also metadata: histories and networks of associations. This is critical to the analysis of designs, which are almost never singular works but rather built from numerous parts, previous designs, and the work of multiple authors. Thus, establishing provenance–the sources, such as participants and processes, involved in producing or delivering an artifact–will be critical to the development of designs in the future.
Comments
This book chapter is published as Doyle, Shelby, and Nick Senske. “Attribution and Artificial Intelligence: Embedding Provenance with Material Metadata.” In Artificial Intelligence (ed. Kyle May et al.). Series: CLOG, 16. [Brooklyn, New York]: CLOG. (2018): 72-73. Posted with permission.