Algorithmic Hubris

Algorithmic hubris is the unreasonable expectation that programmers can create foolproof autonomous systems, that their algorithms will handle edge cases they haven’t anticipated, contexts that shift over time, and failure modes they haven’t imagined.

The term comes from Lazer et al.’s analysis of Google Flu Trends, which succeeded initially but failed as search algorithms, user behavior, and societal context shifted in ways the designers hadn’t anticipated. Shneiderman extends it to the Boeing 737 MAX disaster, where designers believed their MCAS system couldn’t fail so completely that they didn’t document it in the manual or train pilots on manual override.

The pattern: success in controlled conditions breeds overconfidence about uncontrolled conditions. The algorithm worked in testing, so it will work in deployment. It handled last year’s edge cases, so it will handle next year’s.

This isn’t about competence, it’s about the fundamental limits of anticipation in complex systems.

Related: 05-atom—excessive-automation-danger, 07-atom—automation-control-false-tradeoff