Reparations of the horse? Algorithmic reparation and overspecialized remedies

In his seminal article, “Cyberspace and the Law of the Horse,” Frank Easterbrook criticized the scholarly trend of developing overspecialized legal approaches to emerging technologies. Easterbrook argued that these approaches are confusing, shallow, and superfluous. Algorithmic reparation has emerged as a framework for addressing algorithmic systems’ role in inequity and injustice. One understanding of algorithmic reparation is as a method for repairing algorithmic harms. This article examines how this understanding fares against the “law of the horse” critique by posing two questions. First, is algorithmic reparation overspecialized in its methods? Second, is algorithmic reparation overspecialized in the harm it targets? If its methods are too particularized, then algorithmic reparation will only work within a narrow range of circumstances and may undercut a more robust conception of remedies for algorithmic injustice. If the harm it targets is too particularized, then algorithmic reparation will result in incomplete or misguided redress of harms. We determine that algorithmic reparation is not too specific in its methods by demonstrating how–under algorithmic reparation principles–existing methods for reparations can be applied to address algorithmic harm. We also determine that algorithmic reparation can sometimes be too narrow in the harm it targets, which can reduce its effectiveness. When an algorithmic system is both necessary and sufficient for a harm to occur, algorithmic reparation is an effective method of redress. But when an algorithmic system is not necessary and sufficient for a given harm, algorithmic reparation may be incomplete, only temporarily effective, or miss the mark entirely.

Authors

Institutions

Publication Details

Journal
Big Data & Society
Published
2024-09-01
DOI
https://doi.org/10.1177/20539517241270670
Citations
1
Primary Topic
Law, AI, and Intellectual Property
Type
article
Field-Weighted Citation Impact
0.35
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Reparations of the horse? Algorithmic reparation and overspecialized remedies

Lauren M. Chambers, Cedric Deslandes Whitney, Colin Doyle, Gabriel Grill et al.
1 citations
Big Data & Society
Law, AI, and Intellectual Property
0.35
article

Reparations of the horse? Algorithmic reparation and overspecialized remedies

Lauren M. Chambers, Cedric Deslandes Whitney, Colin Doyle, Gabriel Grill, Pelle Tracey, Melissa Alvarez-Garcia
article en
1 citations

Abstract

In his seminal article, “Cyberspace and the Law of the Horse,” Frank Easterbrook criticized the scholarly trend of developing overspecialized legal approaches to emerging technologies. Easterbrook argued that these approaches are confusing, shallow, and superfluous. Algorithmic reparation has emerged as a framework for addressing algorithmic systems’ role in inequity and injustice. One understanding of algorithmic reparation is as a method for repairing algorithmic harms. This article examines how this understanding fares against the “law of the horse” critique by posing two questions. First, is algorithmic reparation overspecialized in its methods? Second, is algorithmic reparation overspecialized in the harm it targets? If its methods are too particularized, then algorithmic reparation will only work within a narrow range of circumstances and may undercut a more robust conception of remedies for algorithmic injustice. If the harm it targets is too particularized, then algorithmic reparation will result in incomplete or misguided redress of harms. We determine that algorithmic reparation is not too specific in its methods by demonstrating how–under algorithmic reparation principles–existing methods for reparations can be applied to address algorithmic harm. We also determine that algorithmic reparation can sometimes be too narrow in the harm it targets, which can reduce its effectiveness. When an algorithmic system is both necessary and sufficient for a harm to occur, algorithmic reparation is an effective method of redress. But when an algorithmic system is not necessary and sufficient for a given harm, algorithmic reparation may be incomplete, only temporarily effective, or miss the mark entirely.

Big Data & SocietyVol. 11(3)
University of Michigan (US), Harvard University Press (US), Loyola Marymount University (US), University of California, Berkeley (US)
Openalex Percentile: Top 33%
Law, AI, and Intellectual Property
0.35
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.