Human-in-the-loop AI is an exciting field with many new applications. Companies are experimenting with ways in which to automate their MLOps pipelines by plugging in a human-in-the-loop to handle manual steps where human insight and intervention may be needed.
Below, we are featuring 25 different use cases of computer vision applications which plug in a human in the loop at different stages of the AI pipeline.
Industrial and manufacturing
1. Quality control: In industrial pipelines for product manufacturing, computer vision systems can detect defects or abnormalities, but humans can step in and make the final decision whether the product should be discarded or not.
2. Process errors: An intervention by a human-in-the-loop may also be needed in case a product gets stuck, a barcode is difficult to locate, or there is an obstacle identified on the conveyor belt. The AI system may not be prepared to deal with edge cases such as these ones.
3. Logistics and warehouses: Similar obstacles could be encountered by logistics robots and drones which have to navigate warehouses. A human operator can step in and determine the right course of action in order to avoid harmful errors.
4. Robotic maintenance: Using robots, many companies are doing inspections of their facilities, solar parks, wind farms, etc. If the robot comes across an unexpected occurrence, it’s important to be able to fall back on a human who can handle the edge case.
5. Recycling and waste processing: Smart waste picking and processing robots may stumble upon very strange artifacts which they may not know how to classify because they are not represented in their training data. By using a human in the loop, companies can efficiently handle such exceptions.
Surveillance and identity
6. Intruder detection: AI systems which monitor business premises may detect intruders or cases of theft or property damaging. In order to make sure that false alerts are not being promoted and create “alert fatigue” in the end users, it’s useful to plug in a human operator who can review every alert before it’s promoted to security guards.
7. Weapon detection: Similarly to the previous use case, AI systems may use infrared and visible light imagery to detect weapons in public spaces or schools, but a human can be involved any time when an alert is triggered in order to verify it.
8. KYC: In automated systems for identity verification, users submit their selfie and a picture of their ID. In order to confirm that this is a real person and to avoid spoofing, AI systems can be used but whenever they are not certain, they may rely on a human to make the last decision.
9. Patient monitoring: In settings such as hospitals or homes for the elderly, it’s useful to have automated systems which detect patients who have fallen on the ground or who need help. However, humans are unpredictable and an AI system may not be prepared to handle each situation, which is where a human can be plugged in.
10. Worker monitoring: Advanced AI systems are now being used to guarantee worker safety by monitoring dangerous conditions and the presence of personal protective equipment. If an alert is triggered, it may be useful to send it to a human operator first for verification instead of promoting it immediately, in case it’s a false positive.
Robotics and autonomous vehicles
11. Last-mile delivery robots: If a delivery robot gets stuck or encounters an unidentified obstacle, it’s useful to be able to revert to a remote human operator who can safely help it navigate the route and reach its final destination.
12. Fruit-picking robots: Many agriTech companies are now using AI for smart fruit and vegetable detection and precise picking. However, depending on the weather conditions, lighting, and other factors, some products may be difficult to distinguish. Why not call a human in the loop?
13. Spraying and harvesting tractors: In order to reach full autonomy for smart tractors, there is a need for a safety net. Whenever the tractor is stuck or is unable to interpret the environment with certainty, a human operator can step in to provide feedback.
14. Drones: Drones are increasingly being used for delivery and autonomous flight, but they may have to confront challenges such as complex landing environments and unmapped terrains. A human in the loop can help them find the perfect landing site.
15. Autonomous cars: Many autonomous vehicle manufacturers, such as Tesla, have shared that whenever their cars come across an unexpected situation, they have a safety driver who can take control over the car in order to navigate the obstacles safely.
Smart cities and environment preservation
16. Traffic monitoring: Smart cameras are nowadays used for monitoring traffic flows and congestion – but whenever an accident happens, the system may need to refer to a human operator who can confirm the alert and directly send it to emergency responders.
17. Wildfire monitoring: Wildfires are becoming increasingly dangerous but may be difficult to spot in the first minutes when they are the easiest to extinguish because of the amount of terrain which has to be monitored. Whenever smoke is detected, a human can make the judgment whether that’s a cloud, smoke from a barbecue, or an actual fire – and alert the fire brigade.
18. Public safety: Monitoring crowd activity in a way which respects personal privacy but also guarantees public safety is essential for today’s smart cities. If there is an accident or suspicious activity detected, it’s best to direct it to a human operator to approve.
19. Environmental monitoring: Computer vision systems can analyze data from cameras and geospatial images in order to detect environmental hazards, oil spills, illegal deforestation, or other problems. These can be sent to a human operator to review and take action.
20. Emergency response: During natural disasters and emergencies, AI systems can provide early warnings and analyze damage levels, but humans need to be involved to make decisions about detected survivors or areas which need emergency help.
Business operations and professional services
21. Document processing: AI systems which process receipts, invoices, or other types of documents may frequently come across handwritten or blurry text which is not readable. In these cases, it’s useful to call a human operator in order to improve the system’s accuracy.
22. Car damage verification: Insurtech companies are now increasingly using AI for automated damage detection for car rentals or for claims processing. However, it’s a non-trivial task to distinguish between scratches and glare, or chipped paint and dirt, which is why a human in the loop may be useful to make the final judgment.
23. Medical diagnosis: Computer vision systems can analyze medical images, such as X-rays or MRI scans, to detect abnormalities, but human doctors are needed to confirm the readings and make the final diagnosis.
24. Retail: AI can analyze video feeds to monitor customer behavior and to enable cashierless checkouts, but some products or actions may be difficult to identify without the involvement of a human operator.
25. Shelf monitoring: In order to ensure proper inventory management, robots and cameras can be used to monitor product availability on shelves. However, whenever there is a mismatch between a product and the expected SKU, a human in the loop can step in to reconcile the differences.
Real-time edge case handling
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