Waste Management, AI and Bias

Businesses and governments are using computer vision to make waste management safer and more efficient. AI models can sort waste quickly, handle hazardous waste, sort waste based on markings and even identify waste items from the air.

However, these important use cases can be subject to bias. Training data that is not varied enough can lead to models that do not perform well in all conditions. Overcoming bias in AI training data leads to waste management systems that work across regions and in diverse conditions. Data annotation experts, like Keymakr, can help waste management AI developers to create powerful, varied training images and video.

Firstly, this blog will look at the useful AI applications that are being used in the waste management sector. Secondly, we will identify some specific bias issues that could impact waste management AI. And finally, we will show how annotation services can help improve datasets and avoid bias.

The importance of waste management AI

Dealing with waste is a vital responsibility that is growing increasingly challenging. As cities and other communities grow and as the need for recycling increases waste management organizations are using AI to streamline their processes:

• Sorting facilities: Sorting facilities can use AI models to automate recycling. Object recognition capabilities mean that machine learning models can identify waste types and sort them accordingly. This technology can be combined with robotics to fully automate the waste sorting processes. This will improve efficiency and keep workers safe.
• Waste management with drones: Landfill sites can be very large and hard to survey. Drones can help waste management organisations to monitor dumping areas and identify specific waste types. AI equipped drones can also spot plastic waste in the ocean, helping clean up and protect fragile ecosystems.
• Handling Dangerous waste products: Medical waste and chemicals are of course difficult and dangerous to handle. So sorting these items can be expensive and time consuming. Computer vision models can be combined with robotic systems to sort hazardous waste without the need for human intervention. These models need precise training data so that they can safely identify and sort dangerous materials.
• Identifying markings on waste items: Many waste items have identifying markings that can be used to sort them for recycling. These markings can also be used by computer vision systems to find waste types and dispose of them correctly. This ability is made possible by image annotation. By highlighting letters and numbers in images human annotators help AI models to reliably identify markings.

Bias problems to be tackled

Bias issues in training data can impact model performance. The following problems need to addressed in datasets for waste management AI:

• Varied weather and light conditions: Waste management sites are often outside and operate all year round. This means that training data has to reflect a wide range of weather and light conditions. This will allow AI models to perform well in rain and at darker times of year.
• Different waste management environments: Waste management practices and facilities can vary from region to region and country to country. Affective AI models should be able to transfer between different cultural contexts. This is why building varied training sets is so important.
• Changing marking conventions: The way in which waste items are marked can also vary significantly. Developers need to be mindful of changing conventions and ensure that their training data reflects diverse practices.

Annotation services are part of the solution

Keymakr supports waste management AI innovators by providing exceptional annotation services. Keymakr’s data collection expertise means that AI companies can assemble large, varied datasets. In-house data creation facilities also allow developers to specify the images and videos that they need. Contact a team member to book your personalized demo today.