Edge AI risks

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

A hand touches a node in an artificial intelligence peripheral network represented by the brain.
Photo: stnazkul / Adobe Stock

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

We see: Don’t Curb Your Enthusiasm: Trends and Challenges in Edge Computing (TechRepublic)

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Use Edge AI cases

There are many potential use cases for AI on the edge. Some of the possible applications include:

  1. Self-driving vehicles: AI at the edge processes data collected by sensors in real time to determine when and how to brake or accelerate.
  2. Smart Factories: Edge AI monitors industrial machinery in real time to detect defects or malfunctions. Cameras also detect flaws in the production line.
  3. Health Care: Wearable devices can detect heart disorders or monitor patients after surgery.
  4. selling by pieces: Store sensors that track customer movement and behavior.
  5. Video analysis: Artificial intelligence analyzes video footage in real time to identify potential security threats.
  6. Face recognition: Edge AI can be used to identify individuals by their facial features.
  7. Speech recognition: AI on the edge is now used to recognize and transcribe spoken words in real time.
  8. Sensor data processing: Edge AI can process data collected by sensors to determine when and how to brake or accelerate.

Edge AI risks

Lost / neglected data

Edge AI risks include data that may be lost or discarded after processing. One advantage of Edge AI is that systems can delete data after processing, which saves money. The AI ​​decides that the data is no longer useful and deletes it.

The problem with this setting is that the data may not necessarily be useless. For example, a self-driving vehicle might be traveling along an empty road in a remote rural area. Amnesty International may consider most of the information collected to be useless and ignore it.

However, data from an empty road in a remote area can be useful depending on who you ask. In addition, the collected data may contain information that may be useful if it is transferred to a cloud data center for further storage and analysis. They can, for example, reveal patterns in animal migration or changes in the environment that would not otherwise be detected.

Increasing social inequalities

Another danger of AI is that it can exacerbate social inequality. 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 faces. If the only source of this data is from social media, then the only people who will be accurately identified are those who are active on social media. This creates a two-tier system in which edge AI accurately recognizes some people while others don’t.

We see: Artificial Intelligence Ethics Policy (TechRepublic Premium)

In addition, only certain groups have access to devices with sensors or processors that can collect data and transmit it for processing by sophisticated AI algorithms. This could lead to a situation where social inequality is increasing: those who cannot afford devices or live in rural areas where there are no local networks will be excluded from the evolving AI revolution. This can result in a vicious cycle, as sophisticated networks are not easy to establish and can be expensive, which means that the digital divide may increase and disadvantaged communities, regions, and countries may lag further in their ability to benefit from the benefits of advanced AI.

poor quality of data

If the sensor data is of poor quality, the results generated by an advanced AI algorithm may also be of poor quality. This could lead to false positive or negative results, which could have serious consequences. For example, if a security camera that uses Edge AI technology to identify potential threats produces a false positive, it could result in innocent people being detained or questioned.

On the other hand, if the data is of poor quality due to sensors that are not well maintained, it can lead to missed opportunities. For example, if a self-driving car is equipped with sophisticated artificial intelligence that is used to process sensor data to make decisions about when and how to brake or accelerate, poor-quality data could lead the car to make poor decisions that could lead to an accident.

Poor accuracy due to limited computational ability

In typical edge computing settings, the high-end hardware is not nearly as powerful as the data center servers they are connected to. This limited computational power can lead to less efficient sophisticated AI algorithms, as they must run on smaller devices with less memory and processing power.

vulnerabilities

Edge AI apps are subject to various security threats, such as data privacy disclosures, adversarial attacks, and confidentiality attacks.

Data privacy disclosure is one of the most important risks of artificial intelligence (edge). Edge cloud stores and processes a large amount of data, including sensitive personal data, making it attractive targets for attackers.

Another danger inherent in the edge of AI is hostile attacks. In this attack, the attacker disrupts the input to the AI ​​system to make the system make an incorrect decision or produce a wrong result. This can have serious consequences, such as causing a self-driving car to crash.

Finally, Edge AI systems are also vulnerable to covert or heuristics attacks. In this attack, the attacker attempts to expose and reverse engineer the details of the algorithm. Once the correct inference is made about the training or algorithm data, the attacker can make predictions about the future input. Edge AI systems are also vulnerable to many other risks, such as viruses, malware, internal threats, and denial of service attacks.

Balancing risks and rewards

Edge AI comes with benefits and risks; However, you can mitigate these risks through careful planning and execution. When deciding whether or not to use advanced AI in your business, you must weigh the potential benefits against the threats to determine what is right for your specific needs and goals.

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