Beyond mystery: Putting algorithmic accountability in context
Critical algorithm scholarship has demonstrated the difficulties of attributing accountability for the actions and effects of algorithmic systems. In this commentary, we argue that we cannot stop at denouncing the lack of accountability for algorithms and their effects but must engage the broader sy...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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SAGE Publishing
2019-02-01
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| Series: | Big Data & Society |
| Online Access: | https://doi.org/10.1177/2053951719826856 |
| _version_ | 1818931866694057984 |
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| author | Elizabeth Reddy Baki Cakici Andrea Ballestero |
| author_facet | Elizabeth Reddy Baki Cakici Andrea Ballestero |
| author_sort | Elizabeth Reddy |
| collection | DOAJ |
| description | Critical algorithm scholarship has demonstrated the difficulties of attributing accountability for the actions and effects of algorithmic systems. In this commentary, we argue that we cannot stop at denouncing the lack of accountability for algorithms and their effects but must engage the broader systems and distributed agencies that algorithmic systems exist within; including standards, regulations, technologies, and social relations. To this end, we explore accountability in “the Generated Detective,” an algorithmically generated comic. Taking up the mantle of detectives ourselves, we investigate accountability in relation to this piece of experimental fiction. We problematize efforts to effect accountability through transparency by undertaking a simple operation: asking for permission to re-publish a set of the algorithmically selected and modified words and images which make the frames of the comic. Recounting this process, we demonstrate slippage between the “complication” of the algorithm and the obscurity of the legal and institutional structures in which it exists. |
| first_indexed | 2024-12-20T04:23:24Z |
| format | Article |
| id | doaj.art-897011a90fee41018a28959c46304a11 |
| institution | Directory Open Access Journal |
| issn | 2053-9517 |
| language | English |
| last_indexed | 2024-12-20T04:23:24Z |
| publishDate | 2019-02-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | Big Data & Society |
| spelling | doaj.art-897011a90fee41018a28959c46304a112022-12-21T19:53:35ZengSAGE PublishingBig Data & Society2053-95172019-02-01610.1177/2053951719826856Beyond mystery: Putting algorithmic accountability in contextElizabeth Reddy0Baki Cakici1Andrea Ballestero2Engineering, Design, & Society, Colorado School of Mines, Golden, CO, USATechnologies in Practice, IT University of Copenhagen, Copenhagen, DenmarkDepartment of Anthropology, Rice University, Houston, TX, USACritical algorithm scholarship has demonstrated the difficulties of attributing accountability for the actions and effects of algorithmic systems. In this commentary, we argue that we cannot stop at denouncing the lack of accountability for algorithms and their effects but must engage the broader systems and distributed agencies that algorithmic systems exist within; including standards, regulations, technologies, and social relations. To this end, we explore accountability in “the Generated Detective,” an algorithmically generated comic. Taking up the mantle of detectives ourselves, we investigate accountability in relation to this piece of experimental fiction. We problematize efforts to effect accountability through transparency by undertaking a simple operation: asking for permission to re-publish a set of the algorithmically selected and modified words and images which make the frames of the comic. Recounting this process, we demonstrate slippage between the “complication” of the algorithm and the obscurity of the legal and institutional structures in which it exists.https://doi.org/10.1177/2053951719826856 |
| spellingShingle | Elizabeth Reddy Baki Cakici Andrea Ballestero Beyond mystery: Putting algorithmic accountability in context Big Data & Society |
| title | Beyond mystery: Putting algorithmic accountability in context |
| title_full | Beyond mystery: Putting algorithmic accountability in context |
| title_fullStr | Beyond mystery: Putting algorithmic accountability in context |
| title_full_unstemmed | Beyond mystery: Putting algorithmic accountability in context |
| title_short | Beyond mystery: Putting algorithmic accountability in context |
| title_sort | beyond mystery putting algorithmic accountability in context |
| url | https://doi.org/10.1177/2053951719826856 |
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