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Artificial intelligence (AI) is a great example of a technology that has taken decades to become an overnight success. The concept has been around in fiction for centuries and in fact since the 1950s. Its transition to the mainstream arguably began at the start of the 21st century.
Over the last few years, the use of artificial intelligence has massively expanded. This expansion has, however, raised some major challenges and ethical considerations. With that in mind, here is a quick guide to the main challenges and ethical considerations in AI-enhanced data centers.
The data center sector has enthusiastically adopted tools powered by AI, generally for tasks involving monitoring and/or rapid response. For example, AI is now routinely used to monitor critical systems (such as power and cooling) to ensure their continued health.
Artificial intelligence has therefore enabled data center operators to make significant improvements to both reliability and efficiency, including cost-efficiency. For data centers to continue to benefit from these improvements, however, they will need to address three main concerns.
Data centers exist to store and/or process data. This data will have varying levels of sensitivity. It is, however, probably fair to assume that most data centers will store and/or process some sensitive data. More specifically, most data centers will store and/or process some personal data. Some data centers may store and/or process highly sensitive personal data such as medical or financial data.
This in itself is already a privacy concern. In very simple terms, any location that is known to contain a lot of valuable assets is going to be of interest to malicious actors. That’s why these locations typically invest heavily in security (including internal security such as staff vetting). The use of artificial intelligence brings new privacy concerns.
The core problem is that AI-powered tools will often need some level of access to data to perform their function. At the very least they will need information about the data. For example, if an AI-powered tool is tasked with managing resources effectively, it needs to know what the resources are.
This means that, essentially, AI-powered tools need to be treated in much the same way as human users. Just as humans are vetted before they are hired, so AI-powered tools need to be vetted before they are deployed. Likewise, just as human users have their access to data controlled, so AI-powered tools should have their access to data granted on the basis of need.
One of the major trends of the 21st century is the push toward data-driven decision-making as a way to eliminate (or at least minimize) inherent (and often unconscious) bias. Unfortunately, bias is still a very real challenge in both human decision-making and the decision-making strategies used by AI-powered tools.
The reason for this is that AI-powered tools are, ultimately, dependent on some level of human input. This means that any bias during the training process will almost certainly result in the AI algorithm showing bias in its own decisions.
At a minimum, this can reduce the efficiency gains artificial intelligence can deliver. For example, it can result in resources being ineffectively allocated due to incorrect assumptions about which users have priority over others. At worst, it can compromise security and/or compliance with data privacy regulations.
The issue of bias has been extensively highlighted in the context of generative AI and the tools used to detect it. As a result, there has been much more emphasis on encouraging diversity in the human teams that create and train AI-driven tools. It is hoped (and expected) that improved diversity will counterbalance the impact of individual bias and, hence, improve decision-making by both humans and AI algorithms.
Transparency and accountability are essentially two sides of the same coin. In other words, you have to know who did something to be able to hold them accountable for it. With artificial intelligence, you have the additional complication that it is impossible to hold an algorithm accountable for its actions.
This is exactly why it is in the highest degree unlikely that AI-powered tools will be allowed to operate autonomously in any key areas any time soon (if ever). They will always need to be under effective human supervision.
For human supervision to be effective, the human supervisor(s) will need to be clear on what their role is. They will also need to be equipped to perform it effectively. This means that the development of human-AI collaboration processes will be key to the deployment of AI-powered tools in data centers.
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Modern data centers typically hold vast quantities of data (big data). This will generally include personal data and highly sensitive business data. Probably at least some of this data will be required for critical applications. This means that security has to be a top consideration for all modern data centers.
Personal privacy and business confidentiality have become two of the biggest considerations of modern society. Both privacy and confidentiality depend on effective security, including effective network security.
The term “big data storage” refers to the storage of vast quantities of data. Big data storage typically depends on data centers as it requires very demanding infrastructure and processes....
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