For decades, social researchers have argued that there is much to be learned when things go wrong.¹ In this essay, we explore what can be learned about algorithms when things do not go as anticipated, and propose the concept of algorithm trouble to capture how everyday encounters with artificial intelligence might manifest, at interfaces with users, as unexpected, failing, or wrong events. The word trouble designates a problem, but also a state of confusion and distress. We see algorithm troubles as failures, computer errors, "bugs," but also as unsettling events that may elicit, or even provoke, other perspectives on what it means to live with algorithms -- including through different ways in which these troubles are experienced, as sources of suffering, injustice, humour, or aesthetic experimentation (Meunier et al., 2019). In mapping how problems are produced, the expression algorithm trouble calls attention to what is involved in algorithms beyond computational processes.

With the Echo listening constantly for the wake word (e.g., "Alexa"), it is conscious of everything you are saying. Nothing is actually recorded and sent to the Amazon cloud until the wake word has been heard, then recording starts (including a clip that spans a few seconds before the wake word was spoken). This is set up so that Echo can be continually learning how you are using Alexa. Becoming familiar with the ways you are interacting with the device is the most effective way to improve the product over time.