The Data Blog
How Synthetic Data Can Save AI
According to VentureBeat, AI is facing several critical challenges. Not only does it need huge amounts of data to deliver accurate results, but it also needs to be able to ensure that data isn’t biased, and it needs to comply with increasingly restrictive data privacy regulations.
We have seen several solutions proposed over the last couple of years to address these challenges, including various tools designed to identify and reduce bias, tools that anonymize user data, and programs to ensure that data is only collected with user consent. But each of these solutions is facing challenges of its own.
Now we’re seeing a new industry emerge that promises to be a saving grace: synthetic data. Synthetic data is artificial computer-generated data that can stand-in for data obtained from the real world. A synthetic dataset must have the same mathematical and statistical properties as the real-world dataset it is replacing but does not explicitly represent real individuals. Think of this as a digital mirror of real-world data that is statistically reflective of that world. This enables training AI systems in a completely virtual realm. And it can be readily customized for a variety of use cases ranging from healthcare to retail, finance, transportation, and agriculture.
Over the last few years, there has been increasing concern about how inherent biases in datasets can unwittingly lead to AI algorithms that perpetuate systemic discrimination. In fact, Gartner predicts that through 2022, 85% of AI projects will deliver erroneous outcomes due to bias in data, algorithms, or the teams responsible for managing them.
One alternative often used to offset privacy concerns is anonymization. Personal data, for example, can be anonymized by masking or eliminating identifying characteristics such as removing names and credit card numbers from ecommerce transactions or removing identifying content from healthcare records. But there is growing evidence that even if data has been anonymized from one source, it can be correlated with consumer datasets exposed from security breaches. In fact, by combining data from multiple sources, it is possible to form a surprisingly clear picture of our identities even if there has been a degree of anonymization. In some instances, this can even be done by correlating data from public sources, without a nefarious security hack.
Synthetic data promises to deliver the advantages of AI without the downsides. Not only does it take our real personal data out of the equation, but a general goal for synthetic data is to perform better than real-world data by correcting bias that is often ingrained in the real world.
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