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Risks of Machine Learning

Machine Learning is one of the most trending technologies for IT professionals as well as business tycoons. Almost all small, as well as large-sized companies want to run their business using machine learning technology. ML systems have various disruptive capabilities in different sectors such as healthcare, finance, banking, marketing, infrastructure, trading, IT, etc.

Risks of Machine Learning

Although implementing machine learning technology in your business can be difficult and challenging but having deep knowledge of machine learning concepts and their algorithms makes you capable of implementing ML systems significantly.

Although machine learning has become an essential part of today's technology and businesses, still there are so many risks found while analyzing ML systems by data scientists and machine learning professionals. These ML risks may be such as security risk, poor data quality, overfitting, data biasing, lack of strategy and experience, etc. In this topic, "Risks of Machine Learning", we will discuss various risks associated with Machine Learning systems and how can we access machine learning risks. So, let's start with a quick introduction to machine learning and then important risks associated with ML systems.

What is Machine Learning?

Machine Learning is defined as the sub-branch of artificial intelligence (AI) and computer science that deals with making systems capable of automatically learning, predicting, and improving from historical data. It makes machines more intelligent, improving with new data without human intervention.

Types of Machine Learning

Machine Learning help to solve different complex business problems, and based on learning methods, it can be categorised into mainly four types. These are as follows:

  • Supervised Machine Learning
  • Unsupervised Machine Learning
  • Semi-Supervised Machine Learning
  • Reinforcement Learning

Applications of Machine Learning

Machine learning uses a huge amount of structured as well as unstructured data and enables a computer system to predict accurately future events. Machine learning is a broad term and applicable in various industries and have so many applications as well. Below is a list of a few important ML applications:

  • Healthcare and medicine
  • Finance & banking
  • Marketing and trading
  • Personal virtual assistant
  • Speech recognition, text recognition and image recognition
  • Traffic prediction
  • Product recommendation
  • Self-driving cars
  • Email spam and filtering
  • Fraud detection
  • Automatic language translation

Risks of Machine Learning

Nowadays, Machine Learning is playing a big role in helping organizations in different aspects such as analyzing structured and unstructured data, detecting risks, automating manuals tasks, making data-driven decisions for business growth, etc. It is capable of replacing the huge amount of human labour by applying automation and providing insights to make better decisions for assessing, monitoring, and reducing the risks for an organization.

Although machine learning can be used as a risk management tool, it also contains many risks itself. While 49% of companies are exploring or planning to use machine learning, only a small minority recognize the risks it poses. In which, only 41% of organizations in a global McKinsey survey say they can comprehensively identify and prioritize machine learning risks. Hence, it is necessary to be aware of some of the risks of machine learning-and how they can be adequately evaluated and managed.

Below are a few risks associated with Machine Learning:

1. Poor Data

As we know, a machine learning model only works on the data that we provide to it, or we can say it completely depends on human-given training data to work. What we will be input that we will get as an output, so if we will enter the poor data, the ML model will generate abrupt output. Poor data or dirty data includes errors in training data, outliers, and unstructured data, which cannot be adequately interpreted by the model.

2. Overfitting

Overfitting is commonly found in non-parametric and non-linear models that are more flexible to learn target function.

An overfitted model fits the training data so perfectly that it becomes unable to learn the variability for the algorithm. It means it won't be able to generalize well when it comes to testing real data.

3. Biased data

Biased data means that human biases can creep into your datasets and spoil outcomes. For instance, the popular selfie editor FaceApp was initially inadvertently trained to make faces "hotter" by lightening the skin tone-a result of having been fed a much larger quantity of photos of people with lighter skin tones.

4. Lack of strategy and experience:

Machine learning is a very new technology in the IT sector; hence, less availability of trained and skilled resources is a very big issue for the industries. Further, lack of strategy and experience due to fewer resources leads to wastage of time and money as well as negatively affect the organization's production and revenue. According to a survey of over 2000 people, 860 reported to lack of clear strategy and 840 were reported to lack of talent with appropriate skill sets. This survey shows how lack of strategy and relevant experience creates a barrier in the development of machine learning for organizations.

5. Security Risks

Security of data is one of the major issues for the IT world. Security also affects the production and revenue of organizations. When it comes to machine learning, there are various types of security risks exist that can compromise machine learning algorithms and systems. Data scientists and machine learning experts have reported 3 types of attacks, primarily for machine learning models. These are as follows:

  • Evasion attacks:These attacks are commonly arisen due to adversarial input introduced in the models; hence they are also known as adversarial attacks.
    An evasion attack happens when the network uses adversarial examples as input which can influence the classifiers, i.e., disrupting ML models. When a security violation involves supplying malicious data that gets classified as genuine. A targeted attack attempts to allow a specific intrusion or disruption, or alternatively to create general mayhem.
    Evasion attacks are the most dominant type of attack, where data is modified in a way that it seems as genuine data. Evasion doesn't involve influence over the data used to train a model, but it is comparable to the way spammers and hackers obfuscate the content of spam emails and malware.
  • Data Poisoning attacks:
    In data poisoning attacks, the source of raw data is known, which is used to train the ML models. Further, it strives to bias or "poison" the data to compromise the resulting machine learning model's accuracy. The effects of these attacks can be overcome by prevention and detection. Through proper monitoring, we can prevent ML models from data poisoning.
    Model skewing is one the most common type of data poisoning attacks in which spammers categorise the classifiers with bad input as good.
  • Model Stealing:
    Model stealing is one of the most important security risks in machine learning. Model stealing techniques are used to create a clone model based on information or data used in the training of a base model. Why we are saying model stealing is a major concern for ML experts because ML models are the valuable intellectual property of organizations that consist of sensitive data of users such as account details, transactions, financial information, etc. The attackers use public API and sample data of the original model and reconstruct another model having a similar look and feel.

6. Data privacy and confidentiality

Data is one of the main key players in developing Machine learning models. We know machine learning requires a huge amount of structured and unstructured data for training models so they can predict accurately in future. Hence, to achieve good results, we need to secure data by defining some privacy terms and conditions as well as making it confidential. Hackers can launch data extraction attacks that can fly under the radar, which can put your entire machine learning system at risk.

7. Third-party risks

These types of security risks are not so famous in industries as there are very minimal chances of these risks in industries. Third-party risks generally exist when someone outsources their business to third-party service providers who may fail to properly govern a machine learning solution. This leads to various types of data breaches in the ML industry.

8. Regulatory challenges

Regulatory challenges occur whenever a knowledge gap is found in an organization, such as teammates do not aware of how ML algorithms work and create decisions. Hence, a lack of knowledge to justify decisions to regulators can also be a major security risk for industries.

How can we assess Machine Learning Risks?

Machine learning is the hottest technology in the IT world. Although ML is being used in every industry, it has some associated risks too. We can also access these risks when the ML solution is implemented into your organization. Below are a few important steps to assess machine learning risks in your organization. These are as follows:

  • Implement a machine learning risk management framework instead of a general framework to identify the risks in real-time scenarios.
  • By providing training to employees for ML technologies and giving them the knowledge to follow protocols for effective risk management in ML.
  • By developing assessment criteria to identify and manage the risks in business, we can assess the risks in business.
  • ML Risk can also be assessed by adapting the risk monitoring process and risk appetites regularly from past experience or feedback of customers.

Hence, machine learning risks can be identified and minimized through appropriate talent, strategy and skilled resources throughout the organization.


There is no surprise if we say machine learning is a continuously growing technology that is employed in so many industries to make business automated and faster. But as well, as we have recently seen, there are some risks also associated with machine learning solutions. However, data scientists and ML experts are continuously researching more on ML technology and developing new solutions for improving it. In this topic, we have discussed a few important risks associated with ML solutions when implementing them in your business and steps to assess these risks as well. Hopefully, after reading this topic, you have in-depth knowledge of various risks associated with machine learning.

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