MACHINE LEARNING AND DESIGN THINKING ARE “BASICALLY” THE SAME

Summary from the TLDR Design newsletter

Backpropagation in machine learning closely parallels design thinking, as both rely on iterative improvement through feedback loops. Each involves making an initial assumption, testing outcomes, identifying and analyzing errors, refining the approach, and repeating the process. Concepts such as bias, underfitting, and overfitting translate naturally to design: bias reflects designer assumptions, underfitting represents overly generic solutions, and overfitting denotes designs too specific to controlled conditions. Both disciplines emphasize gradual, continuous refinement — learning from feedback to steadily improve outcomes.