The Artificiality Imagining Summit 2024 gathered an (oversold!) group of creatives and innovators to imaging a hopeful future with AI. Tickets for our 2025 Summit will be on sale soon!
This week we dive into learning in the intimacy economy as well as the future of personhood with Jamie Boyle. Plus: read about Steve Sloman's upcoming presentation at the Imagining Summit and Helen's Book of the Week.
Making better decisions is one way to get paid more
Making a good decision implies that we have some idea of what’s true. But we do not have infinite data inputs or processing capacity. We are limited by our lifetimes.
Making a good decision implies that we have some idea of what’s true. As Jill Lepore, professor of history at Harvard University, explores in her podcast series The Last Archive, the “unit of evidence” for discovering truth is data that is now only readable by a machine. The sheer scale of data available to us in the modern world is overwhelming. Humans are impressively efficient thinkers, especially given our limited resources. But we do not have infinite data inputs or processing capacity. We are limited by our lifetimes. We have somewhat unreliable connections to others. It can take us a long time to learn since we can only think in a few dimensions at a time.
But humans can still have the advantage. If relevant data isn’t fully codified, humans have to step in and make judgment decisions. A human has to understand the decision context and wrestle with uncertainty in both the data and in the forecast outcomes. Humans have to make decisions in unstructured, open-ended environments. A human has to make the choice of what error to accept—wasting time predicting the unpredictable or failing to predict the predictable.
Big data also drives more decision making complexity because selection bias increases with data size. As AI makes predictions using larger and larger datasets, it will yield poor predictions if the data is non-representative or is missing important features. Work experience can be thought of as both increasing data quantity and quality which reduces prediction error and allows people to adapt their decisions to the context they find themselves in.
AI lowers the cost of prediction—which means that machines now compete with humans to “learn on the job.” The value of work experience is declining relative to the value of being able to make a good decision. This isn't just a theoretical idea. Good decision making skills are growing in demand—research published last year shows that the share of all jobs requiring decision-making increased from 6 percent in 1960 to 34 percent in 2018, with nearly half of the increase occuring just since 2007. Decision-intensive roles pay better, especially as you get older. Wage growth after age 35 is substantially greater for workers in decision-intensive occupations. In 1960, earnings of full-time workers peaked in the late 30s, compared to the mid-50s today.
Decisions are becoming more difficult (in part because automation and AI are taking over routine decisions) but also because decision contexts are more complex. It’s a kind of a paradoxical double whammy—any error from data is bigger and potentially more difficult to deal with and the decision context is more complex because what’s not codified is more likely to be subjective and require skilled human judgment.
The more experience you have, the more you can make predictions across a larger zone of potential outcomes, as long as you can minimize the impact of cognitive and behavioral biases. Getting better at decision making, improving your ability to recognize and account for biases, making more decisions, and getting good feedback on decisions increases your experience at making them. Decision intensity and experience are complementary.
Helen Edwards is a Co-Founder of Artificiality. She previously co-founded Intelligentsia.ai (acquired by Atlantic Media) and worked at Meridian Energy, Pacific Gas & Electric, Quartz, and Transpower.
