Jenna Burrell (2024), First Monday (vol. 29, no. 4) — DOI-10.5210/fm.v29i4.13630
Key takeaways
- Defining algorithmic injustice as allocative unfairness is a product of computer science’s status and funding, not of the harms themselves. Redefining it as domination, following Iris Marion Young, widens the remedies from code fixes to law, participation, resistance and bans.
Seeded from the abstract and a skim, not a reading. What follows is what the work says about itself.
Burrell argues that ML-ethics research leans heavily toward distributive, computationally tractable definitions of fairness, and she explains the skew sociologically, through how computer science reproduces itself as a field. She holds that allocative fairness is an inherently limited account of justice. She proposes instead Iris Marion Young’s account of injustice as domination and oppression, which she argues fits the algorithmic harms the community already acknowledges better. On that account, legislation, participatory assessment, user repurposing and resistance, and activism for outright bans all count as remedies alongside technical fixes.
Standing in the record
Filed from the survey of 2026-09-25 of work the anthology set aside as out of scope (tier B): 40 seconds of active reading over 7 sessions in the papers-feed tracker. Deferred because nobody has read it closely here yet, not on merit.
It was a boundary case in the survey of work this anthology had set aside as out of scope, and it is filed here rather than in the catchall record, nucleation, under the rule that a work in doubt belongs in the anthology. It is about how ML systems that are already deployed harm people, and how the ML-ethics field’s metrics frame that harm, which is the anthology’s deployment-and-society subject, not a general piece of political philosophy.
Priority for a deeper reading: low — Low reading time (t = 40 s over 7 sessions). The skim covers the whole argument, and the work is not a source for any concrete ML practice.
What a deeper reading should check:
- It is a counterweight to fairness-metric practice. If the anthology holds practices about fairness evaluation, this is the source that says what those metrics cannot measure.
- It argues from secondary literature and theory and has no new empirical data. The claim that fairness work is “overproduced” rests on the cited bibliometrics (Birhane et al. 2022a and others), which a deeper reading should check.
- It is a position paper, so it probably belongs in the anthology as a note (LIT) with a critical stance rather than as the source of a practice.
Access when seeded: Crossref metadata and abstract for the DOI. Full text (HTML) read from First Monday’s article download, including all section heads, the argument of every section, the conclusion and the notes. Crossref gives 2024-04-14 as the publication date, and the article’s citation line gives the issue as “Volume 29, Number 4 - 1 April 2024”. I used the Crossref date because the issue date may be nominal. Received 21 Sep 2023, accepted 10 Mar 2024.