human-enabled machine learning
What It Means
Human-enabled machine learning is when AI systems learn by watching and analyzing how humans actually use software systems in real work situations. Instead of being programmed with rules, the AI observes patterns in human behavior - both successful workflows and common mistakes - to predict what will work best in similar future situations.
Why Chief AI Officers Care
This approach can dramatically reduce training time and costs since you're leveraging existing human expertise rather than creating labeled datasets from scratch. However, it also means your AI will inherit human biases and inefficiencies, and you need robust data governance to ensure you're capturing the right behaviors from the right users.
Real-World Example
A customer service AI watches how experienced support agents navigate through help desk software, noting which sequence of screens and searches lead to successful ticket resolution versus which paths result in escalations or customer complaints, then uses this knowledge to guide new agents or automate similar cases.
Common Confusion
People often confuse this with simple user activity monitoring or basic process mining. The key difference is that human-enabled ML specifically focuses on learning from the success patterns in human software interactions to make predictions, not just tracking what people do.
Industry-Specific Applications
See how this term applies to healthcare, finance, manufacturing, government, tech, and insurance.
Healthcare: In healthcare, human-enabled machine learning involves AI systems learning from clinician workflows, decision patterns, ...
Finance: In finance, human-enabled machine learning observes how traders, analysts, and risk managers interact with trading platf...
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Includes:
- 6 industry-specific applications
- Relevant regulations by sector
- Real compliance scenarios
- Implementation guidance
Technical Definitions
NISTNational Institute of Standards and Technology
"Detection, correlation, and pattern recognition generated through machine-based observation of human operation of software systems capturing successful or unsuccessful operations to enable the creation of a useful predictive analytics capability. "Source: IEEE_Guide_IPA
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