About Black Box AI – 10 Things Business Companies Need to Know About the Technology

Do you wish to know what a black box AI is all about? Its importance in the tech world and what it’s used for? If yes, then you’re at the right spot as we’re about to emphasize the key things you need to know about black box AI…

Before I start, I’ll like to ask us whether we know how all the computer programs and data inputs came to us.

Okay, let’s say people were able to determine solutions to computer and data problems and can solve these problems, but you wonder how and where the solution which they solved came from – Now, this is where the black box AI system comes in place.

Black box AI is an artificial intelligence which comprises a system, device, object, etc, that takes in a given problem (input), solves it and provides information or solution (output) that is either proven or reveals how the problem is been solved.

One thing you need to know about the black box AI is that it’s not transparent; it’s a system that’s not known, so I’ll rather say that this is the major reason why computer scientists and even engineers cannot be able to even detect the algorithms of these systems.

No matter how hard you try, you cannot be able to know what constitutes its operations unless those that built it.

Google is an example of black box AI. With the way the online market is developing day by day, there’s always that high level of competition when it comes to getting top results in search engines.

This helps to drive high-quality traffic and that is why it’s paramount that popular search engine like Google develops their algorithms frequently.

But of course, given the fact that these algorithms are not transparent, you’ll never get the idea of how it works. You only try as much as possible to keep up to date.

Okay, let’s say we create blog websites and put in efforts to ensure that Google approves and ranks them. But the question is “what is the system being used by Google to rank these websites?” The only thing I can tell you is the criteria Google uses to rank web pages cos of course, it’s self-explanatory. But I tell you, I’ve always wondered what they use to check those criteria.

Speaking; Google always kicks against plagiarism in web content through the use of a system called “Plagiarism Checker”, but what we don’t understand is how these systems detect plagiarized content, and how they’re quick to sniff out the contents they think are plagiarized.

Now, that’s what surrounds black box AI and only those that built it knows what it is.

Although people tag a black box algorithm as biased and controversial because of its lack of transparency and secrecy, those that created it cited privacy and security reasons.

Due to the rate of security breaches and data leaks, there’s no doubt that it won’t be possible for them to expose the workings of these algorithms. So we all got to live with that.

Now, let’s look at other things to know about black box AI, so we can still learn further on some key things about the system…

What are the 10 key things about Black Box AI you need to know of?

1. Black box AI can’t be understood no matter how skilled you are, as it is not transparent which makes it hard to understand.
2. Although it may be difficult to understand, it’s much easier and faster to implement compared to Artificial Neural Networks.
3. Black box AI doesn’t explicitly prove how it solves a problem, or how it arrives at a conclusion.
4. The system cannot be able to work on its own but rather needs human beings to work with.
5. Black box AI learns and develops consistently through what is called “Machine Learning (ML)”. This enables the system to constantly develop by examining millions of data collections, where they can be able to interpret the data and make decisions on it.
6. The only thing the system understands is data, rather than the actual real world. What this means is that it can make errors in identifying natural habitats.
7. Black box AI is mostly used for complex issues that cannot be solved by humans because it can easily get the result within just a short period.
8. Black box AI models are mostly used to foster quick decision-making processes, especially in the financial markets.
9. Irrespective of the level of controversies that surround black box AI, its output has always proven to be accurate, which normally comes from the complexity of the algorithms.
10. Black box AI has an opposite, which is known as white box AI. The white box is more transparent and widely appreciated, unlike the black box.

Why do people feel that black box AI is a problem rather than a solution?

Black box AI solves lots of algorithm problems no doubt, but the fact is that lots of people will still see it as a major issue rather than the solution. This has even prompted some companies to ditch the system because of the issue of lack of transparency that surrounds it.

How are we sure that 1+1 = 2, when it can easily be 11? Humans can naturally prove this to be naturally 2, while the black box can only state that it’s true based on data without proper workings.

For me, it’s not a problem as long as it gives me the accurate answer I’m looking for unless it proves otherwise. But for those that make use of artificial neural networks, there’ll always arise a conclusion that the black box is causing more harm than good.

Since people still don’t understand the result (output) it provides, there’ll always be skepticism on how it concluded on that, and whether to trust it in the long run.

Currently, computer engineers, experts, and scientists are working tirelessly to develop artificial intelligence that works like a black box, but in turn, provides a transparent solution that is quite understandable to all.

With this, humans can be able to detect if there’s any error. Artificial intelligence, otherwise known as “Explainable AI” keeps its workings open and clear for understanding, rather than making them hidden.

The common problem that can arise in the use of black box AI is that it can end up providing you with a bias data which will, in turn, affect the business decisions the algorithms have provided for you.

So, making use of an Explainable AI enables you to understand the reason why the black box AI is providing the such result.

What’s the difference between the White Box and the Black Box AI?

Just like I’ve thoroughly explained to you what the black box AI is all about, I’m also gonna be highlighting the white box AI and we’ll see how both compare…

Just like I stated above, the white box AI, unlike the black box AI is transparent in its conclusions. And since lots of people, businesses, and organizations can understand how white box AI solves issues, and how its operations work, it’s been widely adopted at expense of the black box.

At least with this, companies can be able to make quick decisions whenever they suspect any wrongdoing. And as much as fact that finances are widely involved in businesses, the move to accept the operation of the white box has gained popularity.

Generally, the black box deals with the external function of a system before it provides its solution, whereas the white box, on the other hand, considers the internal functioning.

No doubt, in terms of technical ability, and providing index solutions, the white box cannot be compared to the black box when it comes to that, but the fact that it’s something that can be easily understandable for all, it’s considered a welcome development.

But in as much as this, most of these companies that have favored white boxes have not dropped black boxes entirely in their day-to-day operations. The reason is not far-fetched; the black box is fast enough and can process large amounts of data while delivering it at a fast rate compared to the white box.

Also See: About Copy AI – What you can do with it and how it’s used

Why is Black Box Machine Learning Important?

Lots of people have made issues with the use of black boxes, but there are key reasons why we can still consider it as important in our business operations. And most importantly, it helps to fight fraud at a quick rate, which is indeed one thing that makes it still stand out for me.

Given the fact that it processes large data and information at a fast rate, it makes it possible to easily detect any potential risk or any fraud attempt before it even takes place.

Factors to Consider for AI Implementation

We’ve been talking about black-box and white-box AIs, and how companies have been making use of them. But now, we’re going to look at the factors these companies consider before they make use of an AI system.

The factors considered before AI is implemented in any company’s business operations are as follows:

• Such company should first go into proper research and testing to determine whether such AI will be good in terms of helping to solve key issues in their business. This works specifically in big companies where multiple solutions can’t be solved by human initiatives alone. So due to this, a helping hand like the use of an AI algorithm may be required for easy quick-decision making.
• Another factor is by determining whether actually, that AI system can be able to solve the necessary problems required for it. Now, we talked about the issue that comes up with the black box AI, although, it may be accurate but still confusing. So, some companies may put this into consideration to know the one they need to go for or can decide to scrap the system entirely.
• One other important factor is through proper examination or assessment; to determine whether the implementation of the AI solution can help to improve the level of tasks in the business. The loopholes amongst the team, can the AI solution help to cover them? This is another key question and important factor to consider.
• Another thing is to appropriately check how the new system will be integrated into the existing system that’s been in use in the business. Whether both systems can be able to work together or work separately, and how can both be operated to enhance effectiveness.
• You also need to check the installation process and the kind of technology or Operating System it’s required to get it installed.
• Most importantly, you need to rigorously access the security requirements and capability of the AI system you’re set to adopt. Does it require you to update your existing system so it works in line with the AI system?

Conclusion

I know that lots of businesses already fancy the use of white box putting into consideration the controversies that surround the black box, but the fact is that currently, I don’t think it’ll be possible to stop using the black box entirely as it still serves a good purpose in many ways.

Given the fact that it’s developing constantly via the use of Machine Learning, it’s a clear mark that it’s improving in its system processing.

And if you’re running a company that deals in large amounts of data, then I’ll advise you to integrate the black box system as well, even though you’re going for the white box in the long run.

Just like I’ve explained before, the black box is faster to handle large data when you compare it with the white box, and it’ll benefit you in some ways.

At least, in the current state, cracking the black box AI is proving difficult for scientists but we hear that it’s still a work in progress. But the creation of new Explainable AI models that can be able to translate the black box unproven solutions is indeed a positive note.