AI

Having shipped AI products to billions of users at Meta, Microsoft, and now Google, I’ve seen firsthand how trust and safety aren’t just ethical considerations—they’re mission-critical infrastructure that determines product success. Yet these elements often remain hidden beneath the surface, overshadowed by technical capabilities and performance metrics. The Business Case for Trust and Safety When we discuss AI products, especially in enterprise environments, conversations typically revolve around capabilities, speed, and accuracy. However, Google’s own research on responsible AI deployment shows that trust is the single strongest predictor of sustained product adoption, outweighing even performance improvements by a factor of 2.3x. ...

As a product manager who’s been deeply involved with AI development for over a decade, I’ve encountered challenges that rarely make it into the typical “AI ethics” article. Today, I’m pulling back the curtain to share insights you won’t find in textbooks or most online resources. The Unseen Pitfalls The landscape of AI safety is shifting rapidly. With the rise of generative AI and large language models, we’re facing new ethical dilemmas daily. A 2023 survey by the AI Ethics Institute found that 78% of AI practitioners reported encountering unexpected ethical challenges in their work, highlighting the rapidly evolving nature of AI safety concerns. This isn’t just about avoiding biases anymore; it’s about navigating a complex web of unforeseen consequences. ...

Research-backed strategies for building user trust in AI products, drawing from experience at leading tech companies and applicable to enterprise environments like Google.