FairLens is the world's first data-centric software for discovering hidden biases and ensuring data transparency and fairness.
Available now for Python developers to integrate into their existing workflows, collaborate on, and develop new features to improve fairness in machine learning and create more responsible AI.
It takes Synthesized just minutes to learn the deep statistical properties of a dataset and begin generating high-quality and compliant datasets. You can quickly perform manipulation and synthesize new data—whether generating millions of new data points or optimising a dataset for size and coverage.
With the ability to self-service any and all data needs, Synthesized enables you to decrease the time spent preparing data for those critical modelling tasks, analytics projects, proofs of concept and more.
Data products created by Synthesized outperform traditionally anonymised data in terms of privacy compliance whilst maintaining or exceeding the quality of the original data—increasing its utility and value within organisations.
With automated data profiling, robust data privacy and quality metric reporting provide insights into data performance and coverage for critical projects in software testing and development and model training.
Most data platforms simply redact PII, but Synthesized has been designed from the outset to satisfy all legal and compliance constraints without any loss to data quality. Synthesized can be configured to comply with GDPR, CCPA and HIPAA among other data privacy regulations.
Unlike with traditional data anonymisation, Synthesized data can withstand linkage and other de-identification attacks.