Human vs. Machine: framing the right problems for AI to solve | by Elaine



There are tasks humans can do, tasks AI can automate, and a new category of tasks humans can do only with the help of AI, source: Turing Trap.

Successful AI products enable both to do their best work. Points below frame ways to approach building AI capabilities toward useful, valuable, responsibly considered human needs

Redrawn from Stanford Digital Economy Lab + HAI
Redrawn from Stanford Digital Economy Lab + HAI

Data Science and Design have grown from different disciplines, but converge when productizing data-driven capabilities, when AI is called to attention in interfaces.

Building great AI products starts from identifying valuable opportunities. However, literature surrounding AI has traditionally focused on mechanisms, how AI works: GANs, Neural Nets, RAG, etc. There’s little on capabilities, what AI can do for human tasks other than replacing them.

Referred to as the AI Innovation gap, Data Science and ML experts tend to be far removed from users to think of ideas people want, building capabilities looking for a problem to solve. Designers approach problems from the user’s perspective, but have limited understanding of AI’s qualities, coming up with ideas that can’t be built.

AI products need both. From Sam Stone’s Unlearning to Build Great AI Apps, simultaneously work backwards from user problems, and forwards from technology opportunities.

Successful AI products match technology capability with the right human problem to solve.

Any of three points below position humans and AI to each do their best work. This framing simultaneously begins to avoid concerns for fairness, ethics, unintended consequences because they present opportunities where each uniquely excels, instead of human substitutions:

(1) AI does something in a different way from what exists today,


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