What are the Major Limitations of Machine Learning Algorithms?

AI can now think like a human.

Over the last couple of years, major advancements have been made in machine learning and deep learning technologies. 

As a result developers have been able to build AI tools that can actually think like a human. They are able to perform complex tasks, as well as make logical decisions based on logical analysis of data and patterns, a reserve just for humans.

However, despite the huge strides of progress data scientists have made, several limitations still exist for machine learning algorithms.

Apart from the most common data bias that ML algorithms have to contend with when  making decisions, there are a ton of other machine learning limitations that just don’t make it the silver bullet AI engineers, like me, would like you to believe.

In this article, we are going to look at 5 major limitations of machine learning algorithms so you can know when it’s a bad idea to employ ML to solve a problem.

Let’s get started.

1. Ethical Considerations

Can an algorithm be trusted?

In this day and age, it’ll not sound alien to say that we trust and rely on machine learning algorithms for automation, data analysis and subsequent decision making.

But the question that plagues many AI enthusiasts is whether AI can be trusted to always be fair and neutral.

Since machine learning models are built and trained by humans in the first place. This creates a high probability of the human transferring their bias to their code and training methods.

It is one of the machine learning limitations where one of the most common biases in translation is related to gender.

COMPAS is an artificial intelligence algorithm developed by Northpointe that helps predict the probability of a criminal committing an offense again in the future. 

While these forecasts can help a judge make decisions on the future of a criminal based on their previous jail sentences and bail amounts, it also emerged that COMPAS was biased. 

Propublica established that with this algorithm, a black criminal was judged to be more likely to commit a crime again in the future than they actually did. On the other hand, the white criminals were judged less risky than they actually were.

It’s evident, therefore, that while a powerful tool, machine learning algorithms will still learn from the inherent bias in humans and can’t always be counted on to be fair.

2. Limited Data Availability

Machine learning models need data in order to train themselves to a point where they can make any meaningful decisions.

This is for the very reason that in real life, data and situations vary tremendously so for a ML to be able to handle varying conditions on the ground, it has to learn about these differences in advance.

It means that the larger your architecture the more data you’ll need to feed your neural networks in order to train them.

So this presents us with a twofold problem.

First is the availability of this data in the first place, and secondly is the quality of the data that is available. But since data is one of the main pillars of digital transformation, AI is already playing a role in digital transformation by automating some forms of data collection.

Some industries like agriculture where most processes are still manual to this day just can’t collect the data needed for machine learning. So this limits the ability to implement machine learning solutions in such industries.

Zillow, an online real estate marketplace, is an example of poor quality data.

They developed a machine learning model to estimate the value of homes and the probability that they could be renovated. However, the algorithm did not foresee the COVID-19 pandemic, and the decreasing labor shortage.

So the bad data the algorithm was fed led to an overvaluation of home, which made Zillow Offers lose $245 million in Q3 of 2021, because they ended up paying above market values for homes.

It also led to the shutdown of Zillow Offers.

3. Computational Resources

How long does it take to build an ML model?

Creating an AI model that can simulate human behavior is no small feat.

If you are thinking in terms of a couple of hours then you probably have to rethink your AI project, and that’s not putting into consideration the amount of computing resource power needed to run these models.

It is one of the main limitations of machine learning because it just puts the ordinary joe out of the game. You’ll need a ton of time to dedicate to training your models and running backtests to get them to a point where they can be accurate and reliable, often months or even years.

You’ll also find such projects run by a team of programmers and analysts, and not an individual programmer like you’d see in a web development project. This also limits you if you don’t have the resources to put together a team.

Ordinary computers will not be able to run a machine learning model effectively and give timely results. So you need to set up a supercomputer with lots of gigabytes in ram and several cores, and these costs add up even if you rent a cloud solution like Amazon AWS and Microsoft Azure.

So again you’ll need to invest a significant amount upfront in hardware and expertise, which is often out of reach for most startups.

Conclusion

Is machine learning the silver bullet?

There are already so many success stories of machine learning in action than I care to count. So it is not a question of whether ML actually works.

However, we are trying to establish whether it is the magic wand that’s going to wipe all problems or solve all complexities for humanity. The answer is a resounding, no.

I hope through this article you’ve been able to learn about some of the main shortcomings of implementing machine learning and artificial intelligence solutions to everyday problems. 

The main limitations of machine learning lean towards ethics, lack of data and the time and resources needed to build just a simple workable solution.

However, some limitations will soon become downplayed as the cost of hardware goes down, and humans become more aware and make efforts towards building fair and neutral algorithms.