What's on my mind

How do you use your intuition to make decisions?

I’m diving into the role intuition plays in decision-making. Coming from a research background, intuition often gets a bad rap. We should be “insights-driven,” or “insights-informed” - trusting one’s gut isn’t evidence-based.

We assume using intuition means that:

• We’re lacking evidence
• We’re not addressing our biases
• We’re overconfident in our own abilities
• We don’t value transparency in decision-making

But what if it also means that:

• We know when to change our approach with a difficult stakeholder
• We bring creativity into a process to drive innovation
• We take calculated risks when evidence is missing or lacking
• We have a “gut feeling” that someone is good for us, whether in a work partnership, friendship or romantic relationship

I've been compiling literature on this topic. Here are some of my favorites, which will certainly be referenced in future articles:

Thinking in Bets by Annie Duke

Conditions for intuitive expertise: A failure to disagree by Daniel Kahneman and Gary Klein

Decisive Intuition by Rick Snyder

What role does your intuition play in shaping your decision-making process?

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A nod to my academic past

I spent 15 years as an academic researcher before pivoting to in-house roles in tech and now solopreneurship. This background influences my perspective, so I'll share this perspective here - sometimes though my own writing, sometimes though other sources that catch my attention.

This week: A recent article in NPS caught my attention. It's called AI was asked to create images of Black African docs treating white kids. How'd it go? and it discusses how researchers tried unsuccessfully to generate images of Black doctors and white patients in one image.

NPR explains it like this: "The results it produces are, in effect, remixes of existing content. And there's a long history of photos that depict suffering people of color and white Western health and aid workers."

The study authors say it best:

The NPR article ends with a question that has left me pondering: "Whose responsibility it is to challenge biased images and who should be held accountable when AI generates them?"

This is not a new insight into AI, but coming from a research perspective, it reminded me how difficult it is to generate original and status-quo-challenging answers to questions. Humans still struggle with this - let's be careful not to pass off the challenge to AI without serious thought.

The original essay, called Reflections before the storm: the AI reproduction of biased imagery in global health visuals, contains all relevant figures and prompts.