The United States leads the world in waste creation, with each person producing an average of 1,800 pounds per year. Despite efforts to manage these figures, only 24% of waste is currently recycled, and the problem is only projected to get worse.
Where does all this waste go? 1.8 million acres of active landfills in the U.S., in addition to 6 million acres already closed. Along with significant environmental damage, emissions from these sites pose real health risks to nearby communities, increasing the likelihood of health-related issues in children by 12%.
What if Artificial Intelligence (AI) Could Help?
Waste intended to be recycled is typically collected and transported to a materials recovery facility (MRF) where it can be sorted. However, this process can be somewhat messy as incorrectly classified materials nearly always contaminate the recyclables. Additionally, the sorting is still largely manually performed and labor-intensive. Workers constantly race against time to scan and sort recyclables on a fast-moving conveyer belt (picture the iconic chocolate scene from I Love Lucy, insert garbage, and you’ll have an idea of they face).
AI is already changing waste management in some states through automated sorting systems that utilize robotics and machine learning to accurately categorize recyclables and drastically enhance efficiency. It could be a game-changer in terms of sorting and delivering recycled materials due to the low costs of application compared to other systems, minimal operational costs, and higher efficiency of the technology. It is light-speeds faster than hand sorting (at least 60 times quicker by some estimates).
These systems also learn and improve over time to adapt to new types of waste, ensuring the flexibility of the sorting processes even as the composition of waste changes.
AI stands out among previous recycling systems due to its advanced, deep learning capabilities. With its capacity for integration with multi-sensor systems, it can be trained on thousands of waste images. This enables it to classify complex and overlapping objects with more accuracy and identify materials based on properties like size, shape and texture.
It also provides the ability to track recycling figures at never-before-seen levels, giving real-time, incredibly accurate progress reports. This can help measure improvements across recyclers, brands and other key players in the industry as they work towards more sustainable goals.
Some cities have even implemented AI into curbside recycling pickup experiments to remove one source of the recycling problem. In Michigan and Alberta, Canada, tests of AI-powered cameras on the curbside recycling trucks flagged individuals putting incorrect or non-recyclable items into recycling containers. Those identified were sent letters to correct the mistakes, typically using postcards bearing images of landfills and a friendly message reminding them of the rules of their local recycling programs. The trials in Alberta saw a contamination stream in the city’s recycling that started at 68% drop to 9%during its course.
The Potential Downfalls of Recycling AI
As inane as it might first sound, some critics are wary of AI gaining access to our garbage, but for good reason. Trash is a personal subject and it’s unclear how much information AI technology could extract and retain from examining waste. Not only does it include sensitive documents like health or financial records, but waste also reveals how people live their lives. This includes where they shop, what they eat, and where they frequent. That information has the potential to fall into the wrong hands if a city’s AI sorting systems, or the vendors they work with, are hacked – or it’s sold to a third party.
This data could be misused in several ways such as targeted ads or spam calls, not to mention the risk of identity or credit card theft. Cybersecurity experts call this process “mission creep,” when a piece of technology originally intended to do a specific task gradually expands its focus beyond what was initially proposed. Regulators will need to weigh the potential trade-offs of privacy violations and the associated risks against the long-term benefits of these programs. Or they’ll need strict protocols in place to prevent the sale of personal information along with guidelines for what information the new technology can collect.
AI-driven technologies could be a solution to improve America’s recycling rates and help an industry that is in critical need of assistance. Ultimately, it’s important to remember that solving the waste management crisis requires more than just technology. Individuals and corporations must play their part by disposing of waste responsibly and adhering to the principles of reduce, reuse and recycle. Simple actions like separating recyclables from general waste, choosing eco-friendly packing, setting and meeting sustainability goals, and moving away from single-use plastics can make a substantial difference in reducing waste and minimizing the use of landfills.