Author Sarah Khatry

Just as trust needs to be established in our personal and business relationships, it also needs to be established between an AI user and the system. Transformative technologies such as autonomous vehicles will be possible only when there are clear methods and benchmarks to establish trust in AI systems. At DataRobot, we define the benchmark of AI maturity as AI…

In two recent blog posts, I’ve talked about two cornerstones of trusted AI: evaluating model performance and setting up proper operational oversight. In this post, I’ll review the third and perhaps most important step: creating models that are ethical and principled.  When it comes to ethical AI, high-minded principles get you only so far. There is no one-size-fits-all approach. AI…

In an earlier post, I shared the four foundations of trusted performance in AI: data quality, accuracy, robustness and stability, and speed. Today I want to talk about another important aspect of trusted AI: the software and human management that facilitate the use of AI. We think of this as the operational environment of an AI system.  To create an…

AI has moved from the realms of theoretical mathematics and advanced hardware to an everyday aspect of life, decisions big and small. As its development has accelerated and its use proliferated, our requirements for AI systems have gotten more sophisticated.  At DataRobot, we define the benchmark of AI maturity as AI you can trust. Unlike an intrinsic quality such as…

Humble AI is a new feature in DataRobot that protects the quality of your predictions in situations where the model may be less confident. With Humble AI, users create rules for models in deployment for predictions made in real-time. These rules identify conditions that indicate quantifiably that a prediction may be unsure, and then can trigger actions like defaulting to…

Cutting-edge algorithms and new research will continue to drive the advancement of machine learning. However, there’s a more straightforward way of resolving many challenges in machine learning today, especially when it comes to ethics and better alignment with human values. And it is not surprisingly, not focused on the technology, but rather on the people using it. For advanced AI and machine learning systems already in production, the focus is on delivering the intended value of the system, which is no longer a question of leveling up the technology or mathematical techniques behind it. Value-oriented approaches can be supported holistically by both the human expertise involved in creating AI and by the technology itself.

At DataRobot, we believe humility is a key step in building more trustworthy AI models. What do we mean by humility in AI? We mean that even a model built according to data science best practices may still have particular areas of weakness or vulnerabilities when confronted with new data to score. For example, that data may have quality issues,…

At DataRobot, we strongly believe AI is a force for good. From the way deep learning algorithms can create art and writing to its applications for health and medicine, as well as astonishing head-to-head match-ups between AIs and humans in games, AI has made enormous strides towards imitating human behavior. At the same time, we recognize that AI is a…