AI is everywhere at work, yet even experienced users struggle with one question: how do you use these tools safely. This post introduces the emerging trust gap created not by AI systems, but by the humans using them. It offers a practical model for protecting data, strengthening judgment, and preserving what is uniquely human in your work, along with habits that make AI a disciplined and trustworthy partner.
Drawing parallels between AI usage and adolescent curiosity, this post emphasizes the importance of responsible prompting and ethical considerations in AI adoption.
So, you already have a ML model (or are building one) and want to investigate options available to you for evaluating fairness metrics. Where do you start? If you are new to Fairness metrics, I strongly recommend the engaging and easy reading provided at MachinesGoneWrong to understand why it is so difficult to define fairness. At a…
Over the last few weeks, social justice topics have exploded across all media in light of recent unfortunate events in the United States. Hand-in-hand, the AI community has also seen a tremendous amount of conversation across multiple social media platforms on algorithmic bias and its implications. While social bias and AI bias are closely interlinked,…