2022
DOI: 10.1177/20539517221113772
|View full text |Cite
|
Sign up to set email alerts
|

Feeling fixes: Mess and emotion in algorithmic audits

Abstract: Efforts to address algorithmic harms have gathered particular steam over the last few years. One area of proposed opportunity is the notion of an “algorithmic audit,” specifically an “internal audit,” a process in which a system’s developers evaluate its construction and likely consequences. These processes are broadly endorsed in theory—but how do they work in practice? In this paper, we conduct not only an audit but an autoethnography of our experiences doing so. Exploring the history and legacy of a facial … Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1

Citation Types

0
3
0

Year Published

2022
2022
2024
2024

Publication Types

Select...
5
2

Relationship

0
7

Authors

Journals

citations
Cited by 7 publications
(3 citation statements)
references
References 77 publications
0
3
0
Order By: Relevance
“…An algorithmic audit (Benjamin, 2019;Brown et al, 2021;Bucher, 2018;Burrell, 2016) uses experimental methods to analyze a piece of digital infrastructure such as a ranking algorithm on a website. Other applications of algorithmic audits include social media feeds (Bucher, 2018) facial recognition software (Banerjee et al, 2022;Keyes and Austin, 2022), or employment advertising programs (Imana et al, 2021). In situations where access to a given technology is limited, algorithmic audits offer a way to investigate algorithmic governance.…”
Section: Designing An Algorithmic Auditmentioning
confidence: 99%
“…An algorithmic audit (Benjamin, 2019;Brown et al, 2021;Bucher, 2018;Burrell, 2016) uses experimental methods to analyze a piece of digital infrastructure such as a ranking algorithm on a website. Other applications of algorithmic audits include social media feeds (Bucher, 2018) facial recognition software (Banerjee et al, 2022;Keyes and Austin, 2022), or employment advertising programs (Imana et al, 2021). In situations where access to a given technology is limited, algorithmic audits offer a way to investigate algorithmic governance.…”
Section: Designing An Algorithmic Auditmentioning
confidence: 99%
“…This situation can then be hard to correct, as simply removing a badly phrased question from a survey can lead to knock-on changes due (for instance) to priming. This echoes ethical issues with datasets with personal information used in computer science, which can continue being used despite major flaws being found [16].…”
Section: Introductionmentioning
confidence: 99%
“…However, it has been highlighted in several studies that these responses are associated with their own set of challenges because iterations of datasets are still present even after the original set is removed (Peng, 2020). Moreover, the methods of tracing and accounting for harmful datasets, for instance through dataset auditing practices, comes with its own methodological challenges and ethical conundrums (Raji et al 2020; Keyes and Austin 2022). The challenges of hindering the reuse of contested data have also engendered new strategies to protect corporations against data as a ‘toxic asset’ (Schneier, 2016), and new discourses that frame sensitive data as potentially ‘toxic waste’ that can ‘overspill’ (Schwarzkopf, 2020).…”
Section: Introductionmentioning
confidence: 99%