Seascape: A Due-Diligence Framework For Algorithm Acquisition

被引:0
作者
Pitts, Christopher [1 ]
Danford, Forest [1 ]
Moore, Emily [1 ]
Marchetto, William [1 ]
Qiu, Henry [1 ]
Ross, Leon [1 ]
Pitts, Todd [1 ]
机构
[1] Sandia Natl Labs, 1515 Eubank Blvd SE, Albuquerque, NM 87185 USA
来源
ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING IN DEFENSE APPLICATIONS IV | 2022年 / 12276卷
关键词
Machine learning; algorithm assurance; algorithm acquisition; artificial intelligence; algorithm assessment;
D O I
10.1117/12.2643193
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Any program tasked with the evaluation and acquisition of algorithms for use in deployed scenarios must have an impartial, repeatable, and auditable means of benchmarking both candidate and fielded algorithms. Success in this endeavor requires a body of representative sensor data, data labels indicating the proper algorithmic response to the data as adjudicated by subject matter experts, a means of executing algorithms under review against the data, and the ability to automatically score and report algorithm performance. Each of these capabilities should be constructed in support of program and mission goals. By curating and maintaining data, labels, tests, and scoring methodology, a program can understand and continually improve the relationship between benchmarked and fielded performance of acquired algorithms. A system supporting these program needs, deployed in an environment with sufficient computational power and necessary security controls is a powerful tool for ensuring due diligence in evaluation and acquisition of mission critical algorithms. This paper describes the Seascape system and its place in such a process.
引用
收藏
页数:9
相关论文
共 3 条
[1]  
Chacon S., 2014, Apress
[2]  
Cunningham W., 1993, OOPS Messenger, V4, P29
[3]  
Sculley D., 2015, P 28 INT C NEURAL IN