The prediction management framework: ethical, governable, and interpretable deployment of artificial intelligence/machine learning systems

Daniel Grahn; Melonie Richey

doi:10.1117/12.2617772

6 June 2022 The prediction management framework: ethical, governable, and interpretable deployment of artificial intelligence/machine learning systems

Daniel Grahn, Melonie Richey

Author Affiliations +

Proceedings Volume 12113, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications IV; 1211327 (2022) https://doi.org/10.1117/12.2617772
Event: SPIE Defense + Commercial Sensing, 2022, Orlando, Florida, United States

Conference Poster

Abstract

As defense organizations integrate artificial intelligence (AI) into evermore critical operations, especially those near the tactical edge with real-time decision making, the necessity of a standardized, robust framework for deployment and management of AI systems is increasing. In this paper, we propose a Prediction Management Framework (PMF) that aligns with the Department of Defense’s Ethical Principles for AI for ethical, governable, and interpretable deployments. We explore different requirements for the framework with inspiration drawn from various regulatory, safety, and communication standards. In support of these requirements, we offer recommendations and implementation guidance to provide comprehensive visibility into the system.

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Daniel Grahn and Melonie Richey "The prediction management framework: ethical, governable, and interpretable deployment of artificial intelligence/machine learning systems", Proc. SPIE 12113, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications IV, 1211327 (6 June 2022); https://doi.org/10.1117/12.2617772

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