Mitigate discriminatory bias in AI with Modulos at #CES2023

ZURICH, SWITZERLAND November 28, 2022 – The Modulos platform is designed to mitigate discriminatory bias in Artificial Intelligence with a Data-Centric strategy.

While the EU is leading the global push for AI regulation, a lot of activity also is happening in the US with the AI Bill of Rights proposed by the White House and a growing number of local and state laws regulating the use of AI.

Modern AI/ML models inherit the noise and biases encoded in their training data, and these flaws ultimately limit how well these models perform in the real world. Data-centric AI is a shift in philosophy for the development of models from increasing the complexity of the model to improving the quality of the training data. 

Our revolutionary approach at Modulos is to directly link each training sample to model performance and give the user the guidance to clean and de-bias the training data to result in better and fairer models effectively. 

The result is a fast and pragmatic approach to the AI experimentation process, a quick turnaround time, with predictable costs and outcomes. Companies love Modulos because of its low code capability and scalable end-to-end approach which maximizes AI applications performance.

Kevin Schawinski, CEO and co-founder of Modulos AG “We’ve built a platform to help AI practitioners navigate these new regulatory requirements and build AI that’s trustworthy and minimizes discrimination risk.”

Modulos AG has been selected to showcase its technology at the Eureka park in Las Vegas at the CES conference on January 5-8. Supported by the Innosuisse core coaching program, Modulos AG will be hosted at the Swisstech Pavillon in booth 61344. See us live in Las Vegas.

About Modulos

Modulos AG operates in the AI software industry. The platform is designed to make the creation and deployment of trustworthy AI applications which are safe, reliable, transparent and fair, accessible to non-ML experts.

Modulos takes a revolutionary approach by centering the AI journey on data, rather than models, our so-called Data-Centric AI (DCAI). The search for good models can now be automated with our commercial AutoML, while the factor that limits model performance is now the data itself.

We solve this by providing actionable insights on how to address shortcomings in datasets—such as dirty labels, outliers, missing or incorrect labels, and missing and dirty feature values—to instead build ML models that result in desired performance outcomes. This new data-centric approach is based on cutting-edge research done by leading AI researchers, including our co-founder at ETH Zurich, one of the world’s top AI research.