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Human-Machine Teaming For Better Decisions



The OODA loop is a decision-making process that stands for observe, orient, decide, act. Widely used in military, law enforcement and business settings, the OODA model enhances situational awareness, facilitates quicker decision-making, reduces reaction times, and fosters flexibility in dynamic situations. How is AI enhancing the OODA Loop?

 

  • Observe: Machine learning models process complex vast data sets quickly and efficiently including image recognition and text translation. When algorithms are set properly, analysts receive ‘observations’ that are highly targeted and relevant.

  • Orient: Deep learning models (a subset of machine learning) recognize patterns and make predictions therefore can support a more nuanced understanding of a dynamic environment and potential scenarios that may play out.

  • Decide: Algorithms can automate certain decision-making processes based on predefined criteria and help analysts make better decisions faster.

  • Act: Machine learning systems can provide focused real-time information on evolving circumstances and support adaptive responses to changing situations.

 

The two important elements of decision-making are data and judgment. As we deepen the utilization of AI in the areas of Emergency Response and Protective Intelligence, let’s not understate the role of the analyst. Humans are the ultimate source of judgment in how machine learning systems are programmed, how that programming is refined along the way, and how the data is interpreted.


With increasing reliance on AI, human analysts are even more vital when decisions need to be made in threatening situations. Human-machine teaming is an effective way to distribute the cognitive load of decision-making especially when the security and life-safety hang in the balance.









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