Three ways to reduce bias in AI

As humans, we cannot be completely objective when making decisions. Whether we are aware of our subjective thoughts or not, our decisions are always influenced by our personal preferences, values, and perspectives. In contrast, computers make decisions based solely on the data they are fed, which is why applying artificial intelligence to the forecasting and decision-making process can help reduce this subjectivity. However, the data used for machine learning can contain potential biases, causing computers to make discriminatory decisions and produce biased results.

Why is artificial intelligence biased?

Unintentional bias in AI algorithms is both common and problematic. No matter how objective we think the data is, we forget that the people developing these AI systems and creating the data for the machine learning process are all subjective humans at heart. Our opinions, values and knowledge are all part of the data collected, making it possible for the data to have gaps or even biases. Certain groups or communities may have been excluded from the data due to circumstances beyond their control.

We must do everything we can to reduce bias in AI systems, and here are a few ways we can do so.

How to reduce bias in artificial intelligence

Listen to feedback

Recognize the existence of bias in your algorithms from the beginning. Consider the diverse backgrounds, perspectives, and opinions of your end users when building your next model. Listen to their feedback and understand their overall experience to better understand what’s missing, what needs to change, and how to make the model better suit their needs. Simple surveys via social media, personal emails, or project-specific communication channels are a good start to getting user feedback.

Reviewing the training data

The data fed into a machine learning model determines how smart and effective the AI system is. However, more data does not necessarily mean smarter AI. In fact, if too many samples and data sets are fed to the model, it may actually exacerbate its bias. Therefore, data should be carefully reviewed and screened before being fed into the model. The key to ensuring the accuracy of an AI system is to select training data based on data quality rather than quantity.

Maintenance quality assurance

When building a machine learning model, keep an eye on the algorithmic process and review the results in real time to ensure that everything remains consistent as the build continues. It is critical to monitor this process in real time to ensure that unintentional bias does not occur at some point. Identifying and narrowing down the problem early will make it easier to find a solution.

Bias is inevitable

In a perfect world, we could completely eliminate bias in AI without having to worry about discrimination and injustice. In reality, however, bias in AI is a major challenge in the tech sector. Ultimately, machines are built by humans, and the data they learn from ultimately comes from our own cognition and biases. Our task is to identify these biases and understand their sources in order to build systems that minimize bias, and ideally, avoid it altogether.

Arrow Translation can help reduce bias through scalable and secure data collection, annotation, and more. Feel free to contact us to learn more about our solutions.

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