Three ways you can build trust and reduce risks from third-party AI vendors through a purpose-built AI governance strategy.
As a model builder, ask yourself, "What exactly is systems engineering, and how does it apply to my life as an AI practitioner?"
Learn more about synthetic data, what it is, where it is useful, and where it can be harmful. Here are tips to assess the quality of synthetic data for your AI use cases.
As the public becomes more aware of generative AI models, thanks in part to tools like OpenAI's ChatGPT, there has been a surge of fear and concern regarding AI regulation and governance. In this article, we put that fear into context and offer actionable steps toward governance, drawing from historical lessons.
What’s your mindset when building an AI model? Christoph Molnar, a statistician and machine learning expert, explains that our approach to factors such as interpretability and uncertainty is what takes our models beyond mere performance.
What is data management's role in machine learning and AI model systems? We illustrate the importance and process of establishing data controls and data quality prior to building these systems.
The increasing concerns about AI and the need for regulations are prevalent. However, focusing on regulations specific to LLMs can miss the bigger picture of best practices.
Learn more about product development and design to make ethical AI considerations an enabler of innovative products your customers will trust and love.
While many were marveling at the release of OpenAI's GPT-4, Monitaur analyzed the accompanying papers that examined the risks and technical design of its latest engine through the lens of proper governance, responsible use, and ethical AI.
AI governance is a business enabler. Without proper governance practices, deploying AI is like asking your models to keep silent about the reasons they're making decisions that affect your business.
