Synthesized has built the fastest ML-powered bias identification and remediation platform to ensure fairness and transparency in all data-driven applications
We've released the Community Edition of the data platform for Bias Mitigation. It incorporates research and cutting-edge techniques to enable any organisation to quickly identify potential biases within their data and immediately start to remediate these flaws
Misconception 1: Predictive models are designed to discriminate, so removing bias is contrary to this aim.
FALSE: Bias in data can lead to incorrect interpretations of models behaviour, and more importantly can lead to you falling foul of regulatory restrictions.
Misconception 2: Fairness in ML is an abstract topic and doesn’t have implications to the business.
FALSE: The bias manifests itself in poor predictive capability, the loss is in efficiency and in effectiveness.
Misconception 4: Removing sensitive attributes from data will make the dataset unbiased and fair.
FALSE: Causality is often hidden in data.
Misconception 3: Bias only applies to things like race and religion.
FALSE: Bias in data is complex and there are many other types: historical bias, representation bias, measurement bias and many more.
At Synthesized, our platform allows businesses of the modern world, of the future, to work with data safely in a fair manner which is the core underlying principle of our technology.
The Synthesized platform automatically and accurately identifies bias in data in seconds. But that’s only the beginning: we also enable you to automatically remove the bias, too.
Automatically identify bias across data attributes like gender, age, race, religion, sexual orientation, and more
Automatically remove the biases present in an entire dataset using data rebalancing