Challenges in Understanding Human-Algorithm Entanglement During Online Information Consumption.
Most content consumed online is curated by proprietary algorithms deployed by social media platforms and search engines. In this article, we explore the interplay between these algorithms and human agency. Specifically, we consider the extent of entanglement or coupling between humans and algorithms...
| Publicado en: | Perspectives on Psychological Science Vol. 19; no. 5; pp. 758 - 767 |
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| Autores principales: | , , |
| Formato: | Journal Article |
| Publicado: |
Sage Publications Inc.
Sep2024
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=179766024&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 179766024 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 17456916 1WQQ jtl: Perspectives on Psychological Science issn: 17456916 maglogo: N pubinfo: dt: Sep2024 vid: 19 iid: 5 pid: 20732 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 179766024 10.1177/17456916231180809 179766024 ppf: 758 ppct: 9 formats: tig: atl: Challenges in Understanding Human-Algorithm Entanglement During Online Information Consumption. aug: au: Lewandowsky, Stephan Robertson, Ronald E. DiResta, Renee affil: School of Psychological Science, University of Bristol sug: subj: Algorithms Social Media Utilization Communication Research Personnel Cognition ab: Most content consumed online is curated by proprietary algorithms deployed by social media platforms and search engines. In this article, we explore the interplay between these algorithms and human agency. Specifically, we consider the extent of entanglement or coupling between humans and algorithms along a continuum from implicit to explicit demand. We emphasize that the interactions people have with algorithms not only shape users' experiences in that moment but because of the mutually shaping nature of such systems can also have longer-term effects through modifications of the underlying social-network structure. Understanding these mutually shaping systems is challenging given that researchers presently lack access to relevant platform data. We argue that increased transparency, more data sharing, and greater protections for external researchers examining the algorithms are required to help researchers better understand the entanglement between humans and algorithms. This better understanding is essential to support the development of algorithms with greater benefits and fewer risks to the public. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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