Risks of edge AI

AI at the edge can revolutionize business, what do we need to prevent unintended consequences?

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

At the edge, artificial intelligence has many potential use cases. Some possible applications include:

• Self-driving cars: Edge AI processes data collected by sensors in real time to decide when and how to brake or accelerate.
• Smart factory: Edge artificial intelligence monitors industrial machinery in real time and detects abnormalities or failures. The cameras can also detect defects on the production line.
• Healthcare: Wearable devices can detect cardiac abnormalities or monitor post-operative patients.
• Retail: Sensors that track customer movement and behavior.
• Video Analysis: Artificial intelligence analyzes video clips in real time to identify potential security threats.
• Facial Recognition: Edge AI can identify individuals through their facial features.
• Speech Recognition: Edge AI is now used to recognize and transcribe spoken language in real time.
• Sensor Data Processing: Edge AI can process data collected by sensors to decide when and how to brake or accelerate.

Edge AI Risks

Lost/Discarded Data

Edge AI risks include the possibility that data may be lost or discarded after processing. One of the advantages of edge AI is that the system can delete data after processing, thus saving money. Artificial intelligence determines that the data is no longer useful and deletes it.

The problem with this setup is that the data is not necessarily useless. For example, a self-driving car might be driving on an empty road in a remote rural area. The AI ​​may consider much of the information collected to be useless and discard it.

However, data from empty roads in remote areas could be helpful, depending on demand. Additionally, the collected data may contain some useful information if it can be sent to a cloud data center for storage and further analysis. For example, it might reveal patterns of animal migration or changes in the environment that would otherwise go undetected.

increased social inequality

Another marginal risk of artificial intelligence is that it may exacerbate social inequality. This is because edge AI requires data to function. The problem is, not everyone has access to the same data.

For example, if you want to use edge artificial intelligence for facial recognition, you need a database of face photos. If the only source of this data comes from social media, then the only people who can be accurately identified are those who are active on social media. This creates a two-tier system in which edge AI can accurately identify some people but not others.

Additionally, 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 increased social inequality: those who cannot afford devices or live in rural areas without local networks will be excluded from the fringe AI revolution. This can lead to a vicious cycle, because the construction of edge networks is not simple and the cost is high. This means the digital divide is likely to widen, and disadvantaged communities, regions and countries may fall further behind in their ability to harness the benefits of edge AI.

Poor Data Quality

If the sensor data is of poor quality, the results generated by the edge AI algorithm are also likely to be of poor quality. This can lead to false positives or false negatives, with catastrophic consequences. For example, if a security camera using edge AI to identify potential threats generates false alarms, this could result in innocent people being detained or questioned.

On the other hand, if the data quality is poor due to poor sensor maintenance, this can result in missed opportunities. For example, self-driving cars are equipped with edge artificial intelligence that processes sensor data to decide when and how to brake or accelerate. Low-quality data can cause the car to make poor decisions, leading to accidents.

Inefficiency

In a typical edge computing setup, edge devices are not as powerful as the data center servers to which they are connected. This limited computing power can lead to less efficient edge AI algorithms because they must run on devices with less memory and processing power.

Security Vulnerabilities

Edge artificial intelligence applications are subject to various security threats, such as data privacy leaks, adversarial attacks, and confidentiality attacks.

One of the most important risks of edge artificial intelligence is data privacy leakage. Edge clouds store and process large amounts of data, including sensitive personal data, making them an attractive target for attackers.

Another inherent risk of edge AI is adversarial attacks. In this attack, the attacker corrupts the input to the AI ​​system, causing the system to make incorrect decisions or produce incorrect results. This could have serious consequences, such as causing self-driving cars to crash.

Finally, edge AI systems are also vulnerable to confidentiality or inference attacks. In this attack, the attacker attempts to reveal the details of the algorithm and reverse engineer it. Once the training data or algorithm is correctly inferred, the attacker can predict future inputs. Edge AI systems are also vulnerable to a variety of other risks, such as viruses, malware, insider threats, and denial-of-service attacks.

Balancing Risks and Rewards

Edge AI has both benefits and risks. However, these risks can be reduced through careful planning and implementation. When deciding whether to use edge AI in your business, we must weigh the potential benefits and threats to determine what suits your specific needs and goals.

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