Framing fashion : human-machine learning and the Amazon Echo Look

Heather A. Horst, Sheba Mohammid

Research output: Chapter in Book / Conference PaperChapter

Abstract

![CDATA[This chapter examines learning dynamics between humans, human data and machines through the study of a new consumer example of machine learning and AI designed to help people make decisions about what to wear, the Amazon Echo Look. Integrating information from sites such as Instagram, where people posted and reviewed clothing and input from professional stylists, the Echo Look used machine learning and AI to provide people with feedback on different clothing options. It also promised to learn from the participants about their preferences over time to provide more customised advice. Drawing upon a study of 25 women in the USA and Trinidad, we explore the contexts and content that our participants reported the device overlooked and the consequences of these gaps for the ways in which our participants perceived the possibilities of the Echo Look. Our chapter responds to a call to develop nuanced understandings of the human-machine interactions and the potential for more expansive forms of automated decision-making, from the vantage point of the people using these new applications.]]
Original languageEnglish
Title of host publicationEveryday Automation: Experiencing and Anticipating Emerging Technologies
EditorsSarah Pink, Martin Berg, Deborah Lupton, Minna Ruckenstein
Place of PublicationU.K.
PublisherRoutledge
ISBN (Electronic)9781003170884
ISBN (Print)9780367773403
Publication statusPublished - 2022

Open Access - Access Right Statement

The Open Access version of this book, available at https://www.taylorfrancis.com, has been made available under a Creative Commons Attribution-Non Commercial-No Derivatives 4.0 license (https://creativecommons.org/licenses/by-nc-nd/4.0/).

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