Ten years ago Responsible AI meant building responsibly. Today, with generative AI in every profession, responsibility rests with each of us as adopters. The Artificiality Institute’s Become More Essential with AI course reframed my approach, from boundary questions to adaptation states, helping me model exemplar collaboration and guide others.
What does it look like to teach with AI, not just about it? In redesigning my undergraduate course, I treated AI as a responsible teaching assistant. Guided by Anthropic’s 4D framework for AI Fluency, I explored how to delegate tasks, craft ethical prompts, embed discernment into student work, and maintain full human oversight. This post shares the strategies, safeguards, and surprises behind building an AI-integrated course without compromising critical thinking or student agency.
Many treat AI councils as risk shields that slow things down. They should be your greenlight. Good councils protect you from what can go wrong and unlock what can go right. They tie ambition to accountability, strategy to trust, and guardrails to growth. Here’s how to build one that keeps AI aligned, practical, and trusted.
Many HR teams have started AI literacy efforts—but struggle with what comes next. This post offers a practical playbook to move from curiosity to action, using jobs-to-be-done thinking, strategic pilot scoring, and a 90-day implementation engine to build trust, drive adoption, and scale GenAI responsibly within the HR function.
Fluency with AI tools is not the same as fluency in responsible use. In a recent session with high school students, all of whom used ChatGPT daily, few had explored settings like memory or data sharing, and none had used custom instructions. This gap is not limited to students. In this post, I share practical safety practices and a product thinking mindset that apply to anyone building or using AI. Responsible use is the foundation for responsible building.
Frontier firms, Agent bosses and a re-imagined future of work are on the horizon. But scaling AI responsibly will require human-centered literacy, cultural change and leadership evolution. Will your organization transform with intention or struggle to keep up?
At a recent AI/ML leadership summit, executives rated AI literacy as highly important but acknowledged a significant gap in their workforce’s current capabilities. This post explores strategies to bridge this gap effectively.
This post summarizes a talk demonstrating six practical AI applications with actionable prompting strategies, showcasing how AI can enhance productivity in various professional tasks.
Introducing generative AI to engineers in a concise session highlighted the importance of mindset over mere technical demonstration.
Drawing parallels between AI usage and adolescent curiosity, this post emphasizes the importance of responsible prompting and ethical considerations in AI adoption.
Exploring AI’s role in market research, this post details an experiment assessing AI’s capability to assist in brainstorming, structuring, and analyzing market insights effectively.
A little over a month ago, I set out to take a more focused approach to understanding Large Language Models (LLMs). I used them daily for everyday tasks—summarizing articles, refining emails, creating images—but I wanted to go deeper, with the new and powerful models that are now available. I wanted to answer a fundamental question:…
Irrespective of the role you play on your product team, you can champion the change for responsible innovation. I have curated five tools you can use today to begin the dialogue with your product team.
When referring to Responsible AI, Ethical AI, or Trustworthy AI, I often remind myself that it is not the technology that needs to be responsible, ethical or trustworthy. The humans who are defining, designing, developing and deploying the AI technology are ultimately accountable for anticipating and addressing ethical risks from the use of the technology….
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,…
I sometimes ruminate on how my learning journey and insights are different from how I would expect a machine to learn. While the recent launch of OpenAI’s GPT-3 model promises a potential path to Artificial General Intelligence (AGI), I do believe AGI is still a distant dream. Adoption of ethical principles and guidelines while building product is…
One week into my journey of exploring ethical AI in practice, I know I have only scratched the surface of a potentially high impact domain with tremendous possibilities. AI is here to stay – the genie is out of the bottle. However, we are in the very early stages of defining how man and machine…
Working with Artificial Intelligence (AI) and Data Science (DS) over the past few years, the ethical use of this technology has always been on my radar (if you are new to the widespread nature of AI misuse, check out Cathy O’Neil’s insightful and easy read – Weapons of Math Destruction). I firmly believe in the power…