Seascape: a due-diligence framework for algorithm acquisition

Christopher Pitts; Forest Danford; Emily Moore; William Marchetto; Henry Qiu; Leon Ross; Todd Pitts

doi:10.1117/12.2643193

28 October 2022 Seascape: a due-diligence framework for algorithm acquisition

Christopher Pitts, Forest Danford, Emily Moore, William Marchetto, Henry Qiu, Leon Ross, Todd Pitts

Proceedings Volume 12276, Artificial Intelligence and Machine Learning in Defense Applications IV; 122760D (2022) https://doi.org/10.1117/12.2643193
Event: SPIE Security + Defence, 2022, Berlin, Germany

Abstract

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.

Conference Presentation

Citation Download Citation

Christopher Pitts, Forest Danford, Emily Moore, William Marchetto, Henry Qiu, Leon Ross, and Todd Pitts "Seascape: a due-diligence framework for algorithm acquisition", Proc. SPIE 12276, Artificial Intelligence and Machine Learning in Defense Applications IV, 122760D (28 October 2022); https://doi.org/10.1117/12.2643193

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