DataRobot Blog

Learn more about a two-step process to maintain proper AI governance and identify, understand, test for, and mitigate potentially harmful behaviors.

In order for AI projects to be fully embraced, companies must address these concerns, because people need to trust their AI in order for projects to be successful. In this installment of our blog post series, we will explore the question of why trust is an essential component to any conversation around AI.

Evaluating bias is an important part of developing a model. Deploying a model that’s biased can lead to unfair outcomes for individuals and repercussions for organizations. DataRobot offers robust tools to test if your models are behaving in a biased manner and diagnose the root cause of biased behavior. However, this is only part of the story. Just because your model was bias-free at the time of training doesn’t mean biased behavior won’t emerge over time.

Professor William O’Grady joined Ben Taylor, Chief AI Evangelist at DataRobot, on the More Intelligent Tomorrow podcast to discuss language acquisition, the relationship between linguistics and AI, and how AI can help preserve endangered languages.

AI projects have many more unknowns than traditional technology projects. You have to know the right use case to start with and know the value you can expect even before you start. You need to understand what data sources to go after and how to get the data ready. You have to pick the right model to meet expected performance goals. Train it, test it, tune it. The list goes on. While you are trying to figure all this out, organizational leaders expect results from their investments in AI faster than ever before.

The topic of artificial intelligence can be very divisive. Some people think that AI is going to help us to solve new problems. There's another school of thought that believes that AI might one day turn against us.

AI has long been associated with making predictions, but it’s your decisions rather than predictions that impact the world. For example, a grocer might predict that they’ll sell 2,700 mangos at a given store between shipments. This information is powerful, but ultimately the grocer needs to decide how many mangos to order for that store, and the prediction doesn’t tell them exactly what to do.

Accurate pricing is essential to protecting an insurance company’s bottom line. Pricing directly impacts the near-term profitability and long-term health of an insurer’s book of business. The ability to charge more accurate premiums helps the company mitigate risk and maintain a competitive advantage, which, in turn, also benefits consumers.  The methods actuaries use to arrive at accurate pricing have evolved.…

We’ve recently announced the launch of our AI Cloud Platform, a new approach built for the demands, challenges, and opportunities of AI today. AI Cloud brings organizations a unified environment across multiple clouds and data sources into a single, secure system of record. This accelerates the delivery of AI to production for every organization. To celebrate our launch, we went live on LinkedIn with our AI Evangelists to talk about the challenges in the market today that led us to the development of AI Cloud.