Author Scott Reed

Trusted AI as a culture and practice is difficult at any level. The focus in this blog will be on the processes that our stakeholders utilize to create structure, repeatability, and standardization. 

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

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.

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.

As part of a blog post series on the topic of building trust in AI, we recently talked about how  DataRobot organizes trust in an AI system into three main categories: performance, operations, and ethics. In each of these categories is a set of dimensions of trust. The purpose of this blog post is to discuss one dimension of trust…

In a recent blog, we talked about how, at DataRobot, we organize trust in an AI system into three main categories: trust in the performance in your AI/machine learning model, trust in the operations of your AI system, and trust in the ethics of your modelling workflow, both to design the AI system and to integrate it with your business…