Bias is built into modern society and stems from how we’ve developed as a civilization. Over the years, we’ve fought through the socially learned biases that we’ve evolved from our environments and have, to a great degree, bypassed its impact on our ability to judge fairness. Or have we? Algorithms, even those that deal with artificial intelligence, display bias that’s taken directly from their creators. It’s such a prevalent problem that Gartner mentions that by 2022, 85% of the algorithms we use will be erroneous due to bias inherent to the system. There is a way around this stumbling block, however. Synthetic data offers a method of using data that conforms to a data set’s general consensus, allowing a machine to generate data that looks and feels like the real thing. Here, we examine synthetic data in detail and explain how it can help us build a better, fairer AI system.
What is Synthetic Data?
We all know that synthetics refer to anything that’s not naturally occurring. Unite AI explains that synthetic data doesn’t reflect real-world values but is developed by an algorithm to simulate real-world data. Already, skeptics would say that manufactured data can’t give any insight into real-world problems, but this is a misconception. Instead of using the data to develop real-world solutions (which is thinking backward), the synthetic data can be used to improve the fairness of AI, based on a real-world data set but not directly referencing it.
In industries such as healthcare and finance, user data is an essential component for top-class customer service. Businesses are forbidden from sharing this customer data with subcontractors for privacy reasons. Yet, these industries require software companies responsible for creating and innovating their IT systems to have access to information for testing and verification. These financial and healthcare companies aren’t allowed to share customer data without anonymizing it. This anonymization process can be time-consuming and slow down the development and testing of new systems. The solution is to use a synthetic data set. None of the data contained within the data set can be traced back to a real human being since they don’t represent individual data. They create sample data using the trends in a naturally occurring data set and do so faster than anonymizing a real-world data set.
Practical Applications of Synthetic Data in AI Development
AI relies on existing events to learn from. Their output is a function of the world around them. Yet, when we examine AI’s recent achievements, something immediately leaps out at the observer. Many AI-generated solutions are inherently biased. An excellent example is Google’s AI gearing high-paying job ads to men over women. According to the Washington Post, it did so because it assumed that’s how the world works, based on previous data. In the past, coding a program that gave these results meant that it reflected the bias of the programmer. Today’s AI learns on their own, and the prejudice they reflect is inherent in the society around us.
Using synthetic data to train AI gives systems developers access to information that doesn’t reference individual people (and thus protects privacy) but shows the trends in society. This data can be invaluable in helping to shape a less biased world around us. AI can be found in several different industries already, from logo design for business to chatbots dedicated to customer service. With a less biased AI, the responses and results AI produces can shape our society into a more balanced, well-rounded one that takes everyone’s experiences into account. Synthetic data already makes its way into testing for credit and debit card companies. It’s ideal for modelling things like transaction data and credit card spending behaviour. However, while it can do this generally, there’s no way this behaviour could predict something like a financial crash. Crashes and other one-time events are so rare that they fall outside the AI‘s normal operations parameters.
Using Synthetic Data to Create an Equal World
Artificial intelligence runs the risk of reinforcing ideals that already exist in society. How any AI functions revolves around taking data and the results and figuring out how to get raw material to the final product. AI is, at its heart, a black box. We don’t understand how machine learning agents come up with their processing, but it successfully navigates from the start to the end while sparing us the details. This design architecture is efficient, but if the “result” is a society like the one we already have, then AI will enshrine all the existing biases that the system relies upon. Thus, for a more balanced and fairer world cantered on AI, we need to find where these biases are and root them out. Synthetic data gives us a way to model real-world situations accurately and use those to test AI systems for fairness.
