What Are the Major Limitations of Machine Learning Algorithms?

Over the past few years, artificial intelligence (AI) and machine learning (ML) developers have made AI and ML think more like humans, performing complex tasks and making decisions based on deep analysis. Robots performing various jobs for humans are no longer the plot of science fiction films, but the reality of today. However, despite the progress data scientist teams have made in this field, there are still several limitations of machine learning algorithms.

The number of AI consulting agencies has skyrocketed over the past few years, accompanied by a 100% increase in AI-related jobs between 2015 and 2018. This boom has fueled the growth of ML in all kinds of industries.

While ML is very useful for many projects, sometimes it’s not the best solution. In some cases, ML implementation is not necessary, does not make sense, and can even cause more problems than it solves. This article discusses instances where ML is not an appropriate solution.

5 key limitations of machine learning algorithms

ML has profoundly impacted the world. We are slowly evolving towards a philosophy that Yuval Noah Harari calls “dataism”, which means that people trust data and algorithms more than their personal beliefs.

If you think this definitely couldn’t happen to you, consider taking a vacation in an unfamiliar country. Let’s say you are in Zanzibar for the first time. To reach your destination, you follow the GPS instructions rather than reading a map yourself. In some instances, people have plunged full speed into swamps or lakes because they followed a navigation device’s instructions and never once looked at a map.

ML offers an innovative approach to project development that requires processing a large amount of data. But what key issues should you consider before you choose ML as a tool to develop for your startup or business? Before implementing this powerful technology, you must be aware of its potential limitations and pitfalls. ML issues that may arise can be classified into five main categories, which we highlight below.

Ethical concerns

There are, of course, many advantages to trusting algorithms. Humanity has benefited from relying on computer algorithms to automate processes, analyze large amounts of data, and make complex decisions. However, trusting algorithms has its drawbacks. Algorithms can be subject to bias at any level of development. And since algorithms are developed and trained by humans, it’s nearly impossible to eliminate bias.

Many ethical questions still remain unanswered. For example, who is to blame if something goes wrong? Let’s take the most obvious example — self-driving cars. Who should be held accountable in the event of a traffic accident? The driver, the car manufacturer, or the developer of the software?

One thing is clear — ML cannot make difficult ethical or moral decisions on its own. In the not too distant future, we will have to create a framework to solve ethical concerns about ML technology.

Deterministic problems

ML is a powerful technology well suited for many domains, including weather forecasting and climate and atmospheric research. ML models can be used to help calibrate and correct sensors that allow you to adjust the operation of sensors that measure environmental indicators like temperature, pressure, and humidity.

Models can be programmed, for example, to simulate weather and emissions into the atmosphere to forecast pollution. Depending on the amount of data and the complexity of the model, this can be computationally intensive and take up to a month.

Can humans use ML for weather forecasting? Maybe. Experts can use data from satellites and weather stations along with a rudimentary forecasting algorithm. They can provide the necessary data like air pressure in a specific area, the humidity level in the air, wind speed, etcetera, to train a neural network to predict tomorrow’s weather.

However, neural networks do not understand the physics of a weather system, nor do not understand its laws. For example, ML can make predictions, but the calculations of such intermediate fields as density can have negative values that are impossible under the laws of physics. AI does not recognize cause-and-effect relationships. The neural network finds a connection between input and output data but cannot explain the reason they are connected.

Lack of Data

Neural networks are complex architectures and require enormous amounts of training data to produce viable results. As the size of a neural network’s architecture grows, so does its data requirement. In such cases, some may decide to reuse the data, but this will never bring good results.

Another problem is related to the lack of quality data. This is not the same as simply not having data. Let’s say your neural network requires more data, and you give it a sufficient quantity, but you give it poor quality data. This can significantly reduce the model’s accuracy.

For example, suppose the data used to train an algorithm to detect breast cancer uses mammograms primarily from white women. In that case, the model trained on this dataset might be biased in a way that produces inaccurate predictions when it reads mammograms of Black women. Black women are already 42% more likely to die from breast cancer due to many factors, and poorly trained cancer-detection algorithms will only widen that gap.

Lack of interpretability

One significant problem with deep learning algorithms is interpretability. Let’s say you work for a financial firm, and you need to build a model to detect fraudulent transactions. In this case, your model should be able to justify how it classifies transactions. A deep learning algorithm may have good accuracy and responsiveness for this task but may not validate its solutions.

Or maybe you work for an AI consulting firm. You want to offer your services to a client that uses only traditional statistical methods. AI models can be powerless if they cannot be interpreted, and the process of human interpretation involves nuances that go far beyond technical skill. If you can’t convince your client that you understand how an algorithm comes to a decision, how likely is it that they will trust you and your experience?

It is paramount that ML methods achieve interpretability if they are to be applied in practice.

Lack of reproducibility

Lack of reproducibility in ML is a complex and growing issue exacerbated by a lack of code transparency and model testing methodologies. Research labs develop new models that can be quickly deployed in real-world applications. However, even if the models are developed to take into account the latest research advances, they may not work in real cases.

Reproducibility can help different industries and professionals implement the same model and discover solutions to problems faster. Lack of reproducibility can affect safety, reliability, and the detection of bias.

When is a machine learning application not the best choice?

Nine times out of ten ML should not be applied with no labeled data and personal experience. Almost always labeled data is essential for deep learning models. Data labeling is the process of marking up already “clean” data and organizing it for machine learning. If you do not have enough high-quality labeled data, the use of ML is not recommended.

Another example of when to avoid AI is in designing mission-critical security systems because ML requires more complex data than other technologies.

The more data needs to be processed, the greater the complexity and vulnerability. This includes aircraft flight controls, nuclear power plant controls, and so on.

With all its limitations, is ML worth using?

It cannot be denied that AI has opened up many promising opportunities for humanity. However, it’s also led some to philosophize that machine learning algorithms can solve all of humanity’s problems.

Machine learning systems work best when applied to a task that a human would otherwise do. It can do well if it isn’t asked to be creative, intuitive, or use common sense.

Algorithms learn well from explicit data, but it doesn’t understand the world and how it works the way we humans do. For example, an ML system can be taught what a cup looks like, but it doesn’t understand that there is coffee in it.

People feel these limitations, like common sense and intuition, when they interact with AI. For example, chatbots and voice assistants often fail when asked reasonable questions that involve intuition. Autonomous systems have blind spots and fail to detect potentially critical stimuli that a person would immediately notice.

The power of machine learning helps people do their jobs more efficiently and live better lives, but it cannot replace them because it cannot adequately perform many tasks. ML offers certain advantages but also some challenges. 

At Postindustria, we are skilled in overcoming the limitations and have extensive experience in ML development. We are ready to take on your project. Leave us your contact details and we will reach out to discuss your solution.