DataRobot Blog

Robotic Process Automation (RPA) is used to automate manual and time-consuming tasks and solves simple, everyday problems. But an intelligent RPA system—coupled with the power of AI—can solve states' hardest problems. Read our new blog to discover the benefits of IRA for your organization.

Common Challenges of Trusting Your AI blog post will explore some of the biggest obstacles to earning trust in AI systems.

Today’s organizations are up against a great machine learning paradox. Most are investing more than ever in artificial intelligence and machine learning (AI/ML), but far too few have implemented ML models or seen the business impact that AI/ML promises come to life. With businesses pouring resources into AI and machine learning, why are results still so elusive? DataRobot dove deep into the AI/ML strategies of over 400 organizations across industries to find out.

Data literacy is a key component for any organization to be able to scale responsible and trusted artificial intelligence technology. Read more how organizations can cultivate data literacy.

Humans and AI: Bargaining Power. Read more in Colin Priest's blog post and learn how will the need for control affect the success of digital transformation and organizational change.

Trust in AI must be earned. Ideally, business users or consumers that interact with a model and its output displayed in a dashboard should not need to question its authenticity. Unfortunately, we aren’t there yet, and it’s because there are different components to trust, some we have yet to address.

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.

This article explains model evaluation techniques, including Lift Chart, ROC Curve, Prediction Distribution graphs, and the Profit Curve tool.