- When the technology is poorly understood + has high expectations, you leave it open to underwhelming as you don’t have clear signals and targets.
- B/c of the speed, you will have tinkering and UX issues so you will have to mitigate the friction user’s feel
- How to use wins to generate traction
- Ask, what are the differences of degree and kind in this technology that we should consider when approaching a problem. Place these ?s w/in the user experience and how our teams will interact w/ and be affected by the tech.
- Oracle vs. AI; only the latter we see as a threat.
- The idea of AI and trust. Trust as the great lubricant and to what extent do we need to trust technology
- How do you train/test your AI will benchmark and set the standard, “with respect to whom/what?”
- “95% accurate” compared to what? Baselined from? How to calculate ROI from what baseline? What is your measure of performance?
- Bias vs. noise Daniel Kahneman; send that to him, which he’s probably seen
- How we measure AI projects is different from Software and ML model projects
Bias is a statistical concept. If your shots group in a particular quadrant then they’re biased.
Fairness:
- If you’re fair concerning outcome, you might be unfair in respect to the process. (and vice versa)
- “decreasing the accuracy and increasing the fairness”
- Lotteries and counterfactuals