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What are the risks of machine learning?

September 23, 2022

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Machine learning (or ML) is a subfield of artificial intelligence (AI) that involves the application of algorithms, data sets, and statistical analysis in order to form hypotheses and reach conclusions regarding an individual’s behavior. Machine learning is the most cutting-edge technology that business magnates and IT professionals are investing in at the moment. Machine learning technology is something that almost every company, no matter what size it is, wants to implement into their day-to-day operations. ML systems have a variety of capabilities that have the potential to disrupt a variety of industries, including healthcare, finance, banking, marketing, infrastructure, trading, and IT, amongst others. There hasn’t been a better time to enroll in AI and ML courses to stay ahead of the game.

These days, machine learning is playing a significant part in assisting businesses in a variety of areas, including the analysis of structured and unstructured data, the detection of risks, the automation of manual tasks, and the making of data-driven decisions for the growth of businesses, among other things. By applying automation and providing insights to help make better decisions for assessing, monitoring, and lowering risks for an organization, it is able to replace a significant amount of the labor performed by humans and is therefore capable of doing so.

What are the risks of Machine learning?

Although ML has the potential to be a useful tool for risk management, it also comes with significant inherent risk. Only a minor share of businesses are aware of the dangers that come with machine learning, even though nearly half of all businesses are investigating or planning to use the technology. A global survey conducted by McKinsey found that only 41% of organizations believe they are able to comprehensively identify and prioritize risks associated with machine learning. As a result, it is essential to have an understanding of some of the risks that are associated with machine learning, as well as the means by which these risks can be adequately evaluated and managed.

The following is a list of some of the dangers associated with machine learning:

Dangers to Safety

Data protection is one of the most pressing concerns in the field of information technology. Production and revenue are both impacted by security issues within an organization. There are many different kinds of security risks associated with machine learning, and any one of these risks has the potential to put machine learning algorithms and systems at risk.

Protection of personal information and secrecy of data

When it comes to the creation of machine learning models, data is one of the most important factors. We are aware that in order for machine learning models to be trained properly and have the ability to make accurate predictions in the future, an enormous amount of data, both structured and unstructured, is required. As a result, if we want to get good results, we need to make sure that our data is protected by laying out some terms and conditions regarding privacy and keeping it secret. Hackers can carry out data extraction attacks in a way that is undetectable by security systems, which puts your entire machine learning system in jeopardy.

Biased data

When you have biased data, it indicates that human biases may have crept into your datasets and ruined the results. For example, the widely used selfie editor FaceApp was inadvertently trained to make faces appear “hotter” by lightening the skin tone. This was the result of the app being fed a significantly greater quantity of photographs of people with lighter skin tones. FaceApp has since been updated to correct this error.

Poor Data

It is common knowledge that a machine learning model can only learn from the information that is given to it by humans, which means that for it to function properly, the training data must be supplied by humans. What we put in will determine what kind of output we get; consequently, if we enter bad data, the machine learning model will produce unexpected results. Bad data, also known as dirty data, refers to information that cannot be correctly interpreted by a model and can include errors in training data, outliers, and unstructured data.

Overfitting

Overfitting is a problem that frequently arises in non-parametric and non-linear models because these types of models have more leeway to learn their target functions.

An overfitted model is one that fits the training data too precisely, to the point where it is unable to learn the variability that the algorithm requires. This indicates that it will not be able to generalize very well when it is put to the test on actual data.

Insufficient planning and previous experience:

Because machine learning is a relatively new technology in the information technology sector, there is a significant shortage of trained and skilled resources. This presents a significant challenge for many industries. In addition, because there are not enough resources available, there is not enough strategy and experience, which results in the waste of time and money and has a negative impact on the organization’s production and revenue. In a survey that included over 2000 people, it was found that 860 responded that there was a lack of clear strategy, and 840 responded that there was a lack of talent with appropriate skill sets. This survey demonstrates how organizations face challenges in the development of machine learning due to a lack of strategies and experiences that are relevant to the field.

Risks posed by third parties

Because there is such a small chance of these types of security risks occurring in industries, they do not receive as much attention as other types of security risks. When someone outsources their company’s operations to third-party service providers, they typically expose themselves to third-party risks. These service providers might not properly govern a machine learning solution. Because of this, the ML industry experiences many different kinds of data breaches.