machines rely on a large amount of data to learn about performing a task. That is why there are increasingly more datasets containing millions of examples. While this data is very useful for creating amazing models with many features, it can also be harmful. To equip you with this knowledge, let’s explore what possible harm and benefits these millions of 0s and 1s can create.

Benefits of Large Datasets

The possible uses of these datasets are quite well-known in the field. When new datasets are created, other tasks that were once impossible become possible. For example, if there were no huge datasets containing millions of words, language models which can write essays could not exist. Moreover, more datasets mean that researchers have more resources to work with. They can apply this new data with existing data to perform new complicated tasks.

As you know, the success of AI models depends heavily on training data, which consists of the examples that we feed into computers. Large datasets are essential if developers want their AI to be able to deal with most real-world cases. Otherwise, the models would be overfitted, which basically means that they fail to perform tasks on data other than training data. Because of these benefits, new datasets with new features get released frequently. Biases

Biases

are another type of problem that comes with data. However, this time it doesn’t cause problems for the producers and developers. It directly affects the lives of the users, who might be you. When data is biased, it can negatively impact only a specific group of people. Models trained on these examples might even have 100% accuracy, but will still badly affect minorities. Examples will help you understand this better. Some facial recognition machines have much lower accuracy in recognizing dark-skinned people’s faces. This would be a bias because people with dark skin would now be at a disadvantage. They might not be able to access some services that use these facial recognition algorithms.

Moreover, some algorithms that can predict someone’s future would give bad decisions to colored-skin people. For example, a model that could predict the quality of debt from the borrower’s profile might output that an Asian person’s loan is worse in quality than a white person’s loan while everything else is kept constant.

If these biases are not corrected, we will propagate the discrimination that is already present in society. However, this time it might be even harsher and harder to fight against.

Because models are perceived as unbiased and correct by people outside the field, the decisions produced by these computers would not be questioned. Therefore, people who receive unequal treatment will lose their right to call for justice.

Data Leakage

Another problem with data is that it could contain sensitive personal information. This might be intentional or unintentional, but both types should not be tolerated. When a person’s data is leaked, it could lead to their money being stolen, or even worse, their identity might be stolen for other purposes, such as obtaining loans and government benefits.

Other than these two types of biases, there are many more biases that could arise from machine learning models. Although a lot of people would agree that the use of data for model training creates many positive changes in the world, we have to bear in mind that data can also cause harm if created and used improperly.

Lastly, please note that AI is an incredibly powerful technology which can be deployed to perform useful tasks and solve longstanding problems. However, there are also some negative points that we should keep in mind so that we can protect the rights of our users and clients.