The essential role of “human-in-the-loop” in AI & ML
13th December 2024
As artificial intelligence (AI) becomes increasingly embedded in everyday processes, the concept of “human in-the-loop” (HITL) has grown ever more important. Human-in-the-loop can be applied to any deep learning artificial intelligence (AI) project including computer vision, natural language processing (NLP) and transcription.
What does “Human-in-the-Loop” mean for computer vision?
Computer vision systems are designed to perform tasks that replicate human vision and how humans make sense of what they see. The systems take input from a camera, push that through AI use cases and then alert in some form. For instance, at self-checkout, a computer vision system could detect if an item hasn’t been scanned, prompting either an alert to the shopper to self-correct (nudge) or notifying an assistant (blocked checkout). The loop is then closed when the shopper or employee validates the alert.
Human-in-the-loop is the deliberate integration of human oversight in computer vision systems to enhance accuracy, transparency, and reliability.
Why does “human-in-the-loop” matter?
In retail, computer vision AI systems operate in complex environments where excessive alerts or miscalculations can lead to frustration among shoppers or employees, or worse distrust in the system itself. Human oversight trains the system and enables the models to learn what is ‘true’ and what is ‘false’.
For instance, Amazon’s Just Walk Out technology, designed to enable seamless, cashier-less shopping, faced criticism when it was revealed that humans were involved in annotating video images and validating some transactions, which Amazon themselves noted as being necessary to continuously improve the underlying machine learning models.
The issue in this case wasn’t human oversight itself, but rather the lack of transparency on how humans were being used in the loop, which led to confusion over the extent of automation involved and even speculation that AI was not in use.
Balancing efficiency and human oversight
The goal of human-in-the-loop is not to undermine automation but to strike a balance where human judgment complements AI’s speed and efficiency. In retail security, for instance, AI can flag suspicious activities, such as concealment of products. A human can then review a video clip to confirm or dismiss the alert, ensuring that false positives don’t disrupt the shopping experience.
Creating a symbiotic relationship
Rather than seeing human-in-the-loop as a limitation, it should be viewed as an intelligent collaboration. Computer vision AI systems perform repetitive, data-driven tasks with unmatched speed, while humans add contextual understanding to refine those results. This partnership improves accuracy and trust, making systems more responsive to real-world complexity. By recognizing and incorporating this symbiotic relationship, businesses can deploy computer vision and AI solutions that genuinely serve their purpose and users.
