Responsible ML: Balancing Accuracy and Accountability

Recent advances in the world of AI and ML are providing significant opportunities for insurers to improve their business objectives. However, this progress brings additional risks and concerns – about personal privacy, model transparency, and the rationale behind trained models.

In this presentation, Luba Orlovsky, Principal Researcher at Earnix describes the risks that the new era of AI and ML brings and the new tools that Earnix can offer to address them.

She offers a closer look at the topic of synthetic data and how it can provide a statistically similar data set and consistent modeling performance. She also covers the topic of AI/ML model transparency, including why explainability is so important and how Earnix solutions offer explainability to avoid potential bias.

If you’re interested in the topic of fair and responsible AI and ML and want to make sure your models uphold the highest ethical standards, you won’t want to miss this presentation.