全球智库信息集成服务系统

Using metrics not matched to actual priorities obscures system performance and impedes informed choice of the optimal system

• Metric choice should take place before the system is built and be guided by attempts to estimate the real impact of system deployment.

Effectiveness, and therefore the metrics that measure it, can depend not just on system properties but also on how the system is used

• A key consideration for decisionmakers is the amount of resources devoted to the mission outside those devoted to building the system.

The U.S. military and intelligence community have shown interest in developing and deploying artificial intelligence (AI) systems to support intelligence analysis, both as an opportunity to leverage new technology and as a solution for an ever-proliferating data glut. However, deploying AI systems in a national security context requires the ability to measure how well those systems will perform in the context of their mission.

To address this issue, the authors begin by introducing a taxonomy of the roles that AI systems can play in supporting intelligence—namely, automated analysis, collection support, evaluation support, and information prioritization—and provide qualitative analyses of the drivers of the impact of system performance for each of these categories.

The authors then single out information prioritization systems, which direct intelligence analysts' attention to useful information and allow them to pass over information that is not useful to them, for quantitative analysis. Developing a simple mathematical model that captures the consequences of errors on the part of such systems, the authors show that their efficacy depends not just on the properties of the system but also on how the system is used. Through this exercise, the authors show how both the calculated impact of an AI system and the metrics used to predict it can be used to characterize the system's performance in a way that can help decisionmakers understand its actual value to the intelligence mission.

• Chapter One

  Introduction

• Chapter Two

  Tracing Effectiveness from Mission to System

• Chapter Three

  Measuring Performance and Effectiveness

• Chapter Four

  Conclusions

• Appendix A

  Derivations and Technical Details