AI-driven platform helps to remove bias in data
As businesses become more reliant on data, ensuring that it's free of bias is essential to meeting compliance requirements as well as delivering useful insights.
A new AI-driven platform from Synthesized has been designed to understand a wide array of regulatory and legal definitions relating to contextual bias. It can automatically identify bias across data attributes like gender, age, race, religion, sexual orientation, and more.
The company is releasing the Community Edition for Bias Mitigation as a freemium version, incorporating AI research and cutting-edge techniques to enable any organization to quickly identify potential biases within their data and immediately start to remediate these flaws.
Dr Nicolai Baldin, CEO and founder of Synthesized says, "The reputational risk of all organizations is under threat due to biased data and we’ve seen this will no longer be tolerated at any level. It's a burning priority now and must be dealt with as a matter of urgency, both from a legal and ethical standpoint. Synthesized's Community Edition for Bias Mitigation is one of the first offerings specifically created to understand, investigate, and root out bias in data. We designed the platform to be very accessible, easy-to-use and highly scalable, as organizations have data stored across a huge range of databases and data silos."
In addition the platform offers another extremely powerful feature in the ability to automatically remove the biases present in an entire dataset -- a process called rebalancing. While there are a number of existing, limited techniques to rebalance biased data, Synthesized has developed a proprietary algorithm within its platform that is quicker and more accurate. The AI-driven platform has the ability to make randomized changes, at scale, to an original, biased dataset to construct a new, entirely synthetic dataset.
Users can upload a structured data file, like an Excel spreadsheet, to kick off the analysis process. Thay can also connect to relational database services including AWS, Azure, Google Cloud, Oracle, and others, to build custom datasets for analysis. The platform learns the structure of the data in real-time, and the analysis process can crunch over four million rows of data in roughly ten minutes.
Once the analysis is complete, users are provided with a Synthesized Total Fairness Score that shows what percentage of the dataset contained biased data. The platform also highlights areas of the data in which bias was detected.
You can find out more on the Synthesized site.
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