Artificial Intelligence (AI) systems follow a set of criteria and initiate specific actions without deferring to a human expert. The Humans in the Loop (HITL) AI systems, in contrast, refers to a human at critical intersections/crossroads before initiating any action.
Human involvement in AI systems is of two types. In one, AI defers to a human for key decisions, whether or not it knows what to do. In another, AI defers to a human when it cannot decide or offer an answer to a problem, where AI logs the human's response and may act automatically under similar circumstances in the future.
An AI system receives relevant data and stores it according to a specific indexing system or functional design in the HTIL process. For instance, before taking action the system assigns a confidence score to its algorithm variables, describing the accuracy of the judgment made by the AI system. When the confidence value drops below a predefined threshold, the system defers the decision/judgment call to a human. This decision will be applied to that instance and help make the algorithm smarter – the machine learns by interacting with humans.
The consensus among software developers and computer scientists is humans should still be involved in the automation process of critical business functions. Even though AI systems are getting smarter, they lack specific capabilities to integrate long-term qualitative and quantitative objectives. Machine learning accuracy increases by combining machine learning with human interventions for complex business situations.
In the long haul, this signifies that although AI systems are getting smarter, there will always be a place for humans to work alongside them because AI systems have limits in understanding long-term strategy and nuanced business objectives.
Therefore, it becomes critical to involve humans so that they understand what the AI can and cannot perform. It may also include building safety mechanisms for AIs to override humans and vice versa, depending on circumstances.
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