7 Notable Business Uses of Machine Learning

Here are some business uses of machine learning that help clarify how it is being used by organizations today.

Machine learning is a branch of artificial intelligence that deals with the design and development of algorithms that can learn from and make predictions on data. These algorithms can find patterns and insights to make better decisions and predictions.

Business Uses of Machine Learning

Machine learning is used in almost every way you can think of, including:

• Improving search engines: Search engines like Google use machine learning algorithms to improve the accuracy of their results. The algorithms are constantly being refined as new data is collected

• Predicting crime: Police departments in the US are using machine learning algorithms to predict where and when crimes are likely to occur. This information is then used to deploy resources more effectively.

• Detecting fraud: Banks and financial institutions are using machine learning to detect fraudulent activity, such as money laundering and identity theft.

• Recommending products: Online retailers like Amazon use machine learning algorithms to recommend products to customers based on their past purchase history.

• Digital advertising: ML is constantly being refined to help display the most relevant adverts to users, which can result in high ROIs for businesses.

• Self-driving vehicles: Self-driving cars are becoming a reality thanks to machine learning and companies like Tesla and Cruise use ML to constantly improve their technologies, making driving safer for everyone.

• Education: Online education platforms use machine learning to personalize each student’s experience and help tailor questions to each student’s level of understanding. This not only helps teachers improve their ability to teach but also measures student performance more accurately.

Threats Posed by Business Uses of Machine Learning

Machine Learning (ML) is a powerful tool that has quickly become pervasive in our society and can be found in the tools we use daily (e.g., LinkedIn, Facebook, Instagram, TikTok).

But with great power comes great responsibility, and it’s important to consider some societal implications of using machine learning algorithms.

Experts have raised concerns about the potential downsides of machine learning. The risks associated with ML fall into 3 categories: data risks, security risks, and employment risks.

• Data:

Data risks refer to the dangers posed by incorrect or misunderstood data. For example, if a machine learning algorithm is trained on data that is biased, it will learn to be biased as well.

This can lead to serious consequences, such as inaccurate credit scores that discriminate against certain groups of people or even manipulating people’s behavior via the content they see on social media.

An example of business uses of machine learning that had a massive implication in the political world, with its use of data, is related to the Cambridge Analytica scandal in 2016. This one is said to have impacted who people voted for.

• Privacy and Security:

Security risks are also a concern as with ML/AI systems becoming more complex, they become more difficult to protect from malicious activities. For example, in China, facial monitoring is being used to track people’s behavior and emotions, leading to concerns about the government controlling its citizens.

This has led to major privacy and human rights violations, as many citizens have inexplicably had their rights taken away (i.e., not being able to use public transportation, random police searches, denied services at certain shops).

Another worry is that machine learning could be used to manipulate people, as companies could analyze a person’s activity and then find patterns that could be used to exploit that person’s weaknesses.

Another concern is that machine learning could be used to discriminate against certain groups of people. For example, if an employer were to use machine learning to screen job applicants, they could potentially be biased against applicants from certain demographics.

• Employment:

One major concern is that ML may lead to large-scale job losses as machines become increasingly capable of performing tasks traditionally done by human workers.

This could result in a significant increase in unemployment, as workers cannot find jobs that are not at risk of being automated.

Another concern is that AI will lead to wage stagnation or decreases as businesses look to reduce labor costs by replacing human workers with cheaper machines. This could further worsen inequality, as those struggling to make ends meet may be hit the hardest.

These are just some of the concerns raised about the business uses of machine learning. As technology continues to develop, these concerns must be addressed so that everyone can benefit from the potential growth of machine learning.

One way that the US has started to address business uses of machine learning is by introducing regulation. In February 2022, US Senators introduced a bill called the Algorithmic Accountability Act that seeks to regulate and hold accountable organizations’ use of machine learning for decision-making. This looks like a step in the right direction. More to come.