Trust Is Not Enough: Accuracy, Error, Randomness, and Accountability in an Algorithmic Society: Searching for improved algorithmic accountability.
The article discusses the need for accountability from those responsible for search algorithms, including software engineers, corporate officers, institutions, and licensing agencies. Challenges in developing a mechanism for accountability in algorithmic systems are presented, as is the need to addr...
| Publicado en: | Communications of the ACM Vol. 66; no. 6; pp. 42 - 45 |
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| Formato: | Artículo |
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Association for Computing Machinery
Jun2023
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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=hlh&AN=163907052&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 163907052 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 00010782 ACM jtl: Communications of the ACM issn: 00010782 maglogo: N pubinfo: dt: Jun2023 vid: 66 iid: 6 pid: 68 pub: Association for Computing Machinery artinfo: ui: 163907052 10.1145/3565479 ppf: 42 ppct: 3 formats: tig: atl: Trust Is Not Enough: Accuracy, Error, Randomness, and Accountability in an Algorithmic Society: Searching for improved algorithmic accountability. aug: au: Moss, Emanuel affil: Sociotechnical Systems Research Scientist at Intel Labs and former Postdoctoral Fellow at Cornell Tech and Data and Society Research Institute, New York, NY, USA su: Search algorithms Computer software development Software reliability Responsibility Trust Government regulation sug: subj: Search algorithms Computer software development Software reliability Responsibility Trust Government regulation ab: The article discusses the need for accountability from those responsible for search algorithms, including software engineers, corporate officers, institutions, and licensing agencies. Challenges in developing a mechanism for accountability in algorithmic systems are presented, as is the need to address the potential harms caused by algorithmic systems. A significant barrier to accountability includes shifting the blame for errors to external, uncontrollable factors, which the authors refer to as scapegoating. pubtype: Periodical doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y dt: @attributes: year: 2023 holdings: @attributes: islocal: N |
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