How to Investigate an Algorithm
Abstract
Algorithmic auditing has the potential to decrease bias and prevent or fix the harms caused by artificial intelligence.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Responsibility obligations can be technically enforced through mandatory algorithmic auditings integrated into development workflows, requiring continuous monitoring of AI outputs for community impact and automated reporting to Indigenous oversight bodies.
“Responsibility obligations can be technically enforced through mandatory algorithmic auditings integrated into development workflows, requiring continuous monitoring of AI outputs for community impact and automated reporting to Indigenous oversight bodies (see how algorithmic auditing can reduce biases Broussard 2023).”
From Preventing AI extractivism: the case for braiding indigenous data justice with ABS for stronger AI data governance (2026) · cites this paper as Broussard 2023
Cited by
Sorted by citations
Schulz, M., & Loewen-Colón, J. (2026) · AI & SOCIETY · doi:10.1007/s00146-026-02931-z · Cited by 1 · see citing works
- Authors
- Broussard, M.
- Corresponding author
- Broussard, Meredith
- ORCID
- Broussard, Meredith
- Venue
- Issues in Science and Technology
- Publisher
- Arizona State University
- Published
- 2023-07-03
- Volume
- 39
- Issue
- 4
- Pages
- 85-89
- ISSN
- 0748-5492 (ISSN-L)
- OpenAlex
- W4383197335
- Cited by
- 25
- References
- 0
- Citations by year
- 2026: 1 · 2024: 2 · 2023: 22
- FWCI
- 6.27
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Issues in Science and Technology (publishedVersion)
- Topics
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Social Sciences › Political Science and International Relations › Artificial Intelligence in Law
- Keywords
- algorithmic auditing, algorithmic accountability, algorithmic bias, algorithmic transparency, algorithmic fairness
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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