Fighting bias in AI systems is an increasingly big topic – and challenge – for business. Drafting a set of principles is a good start, but when it comes to bridging the gap between the theory and the practical application of responsible AI, organizations often find themselves at a loss.
London-based startup Synthesized set out to ease that challenge, and has now launched a tool that aims to quickly identify and mitigate bias in a given dataset. For data scientists working on an AI project, the company has built a platform that scans datasets in minutes, and provides an in-depth analysis of the way different groups of people are identified within that dataset.
If, in comparison to the rest of the dataset, a particular group is disproportionately tied to a criteria that generates bias, Synthesized's software can flag the issue to the user. The technology also generates a "fairness score" for the dataset, which varies from zero to one and reflects how balanced the data is overall.
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As the name suggests, Synthesized has also developed a synthetic data generation technology, which is used at the end of the process to re-balance the dataset with artificial data that fills in the gaps where bias was identified, to make sure that every group of people is represented fairly.
Synthesized's founder Nicolai Baldin told ZDNet: "By creating these simulated and curated high-quality datasets, you can build better services. We wanted to show that it is possible to make the dataset fairer without lowering the quality of the data. In fact, the results of AI models will improve because those groups that were missing will be represented."
The process is seemingly straightforward. Synthesized's