The risks of edge AI

Artificial intelligence at the edge can revolutionize your business, but what do you need to prevent unintentional consequences?

With the increasing demand for faster results and real-time insights, businesses are turning to edge artificial intelligence. Edge AI is a type of AI that uses data collected from sensors and devices at the edge of a network to provide actionable insights in near-real-time. While this technology offers many benefits, there are also risks associated with its use.

There are many potential use cases for artificial intelligence at the edge. Some possible applications include:

Edge AI risks include data that may be lost or discarded after processing. One of the advantages of edge AI is that systems can delete data after processing, which saves money. The AI determines that the data is no longer helpful and deletes it.

The problem with this setup is that data may not necessarily be useless. For example, an autonomous vehicle may drive along an empty road in the remote countryside. The AI may deem most of the information collected useless and discard it.

However, data from an empty road in an outlying area can be beneficial depending on whom you ask. In addition, the data collected may contain information that may be useful if it makes it to the cloud data center for storage and further analysis. It could, for example, reveal patterns in animal migration or changes in the environment that would otherwise go undetected.

Another edge AI risk is that it can exacerbate social inequalities. This is because edge AI requires data to function. The problem is that not everyone has access to the same data.

For example, if you want to use edge AI for facial recognition, you need a database of photos of faces. If the only source of this data is from social media, then the only people who will be accurately recognized are those who are active on social media. This creates a two-tiered system in which edge AI accurately recognizes some people while others are not.

In addition, only certain groups have access to devices with sensors or processors that can collect and transmit data for processing by edge AI algorithms. This could lead to a situation where social inequality increases: Those who can’t afford the devices or live in rural areas where local networks don’t exist will be left out of the edge AI revolution.