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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Who Audits the Auditors? Recommendations from a field scan of the algorithmic auditing ecosystem
Sasha Costanza-Chock, Inioluwa Deborah Raji, Joy Buolamwini
2022 ACM Conference on Fairness, Accountability, and Transparency (2022), pp. 1571-1583
Open Access | Times Cited: 96

Showing 1-25 of 96 citing articles:

Sociotechnical Harms of Algorithmic Systems: Scoping a Taxonomy for Harm Reduction
Renee Shelby, Shalaleh Rismani, Kathryn Henne, et al.
(2023), pp. 723-741
Open Access | Times Cited: 94
Auditing large language models: a three-layered approach
Jakob Mökander, Jonas Schuett, Hannah Rose Kirk, et al.
AI and Ethics (2023) Vol. 4, Iss. 4, pp. 1085-1115
Open Access | Times Cited: 85
Auditing of AI: Legal, Ethical and Technical Approaches
Jakob Mökander
Deleted Journal (2023) Vol. 2, Iss. 3
Open Access | Times Cited: 44
AI auditing: The Broken Bus on the Road to AI Accountability
Abeba Birhane, Ryan Steed, Victor Ojewale, et al.
(2024), pp. 612-643
Open Access | Times Cited: 24
Ethical governance of artificial intelligence for defence: normative tradeoffs for principle to practice guidance
Alexander Blanchard, Christopher E. Thomas, Mariarosaria Taddeo
AI & Society (2024)
Open Access | Times Cited: 17
Walking the Walk of AI Ethics: Organizational Challenges and the Individualization of Risk among Ethics Entrepreneurs
Sanna J. Ali, Angéle Christin, Andrew Smart, et al.
2022 ACM Conference on Fairness, Accountability, and Transparency (2023), pp. 217-226
Open Access | Times Cited: 34
The challenges of integrating explainable artificial intelligence into GeoAI
Xing Jin, Renée Sieber
Transactions in GIS (2023) Vol. 27, Iss. 3, pp. 626-645
Open Access | Times Cited: 23
Black-Box Access is Insufficient for Rigorous AI Audits
Stephen Casper, Carson Ezell, Charlotte Siegmann, et al.
2022 ACM Conference on Fairness, Accountability, and Transparency (2024), pp. 2254-2272
Open Access | Times Cited: 15
Auditing Work: Exploring the New York City algorithmic bias audit regime
Lara Groves, Jacob Metcalf, Alayna Kennedy, et al.
2022 ACM Conference on Fairness, Accountability, and Transparency (2024)
Open Access | Times Cited: 10
A Framework for Assurance Audits of Algorithmic Systems
Khoa T. Lam, Benjamin Lange, Borhane Blili-Hamelin, et al.
2022 ACM Conference on Fairness, Accountability, and Transparency (2024) Vol. 38, pp. 1078-1092
Open Access | Times Cited: 9
End-User Audits: A System Empowering Communities to Lead Large-Scale Investigations of Harmful Algorithmic Behavior
Michelle S. Lam, Mitchell Gordon, Danaë Metaxa, et al.
Proceedings of the ACM on Human-Computer Interaction (2022) Vol. 6, Iss. CSCW2, pp. 1-34
Closed Access | Times Cited: 36
Sociotechnical Audits: Broadening the Algorithm Auditing Lens to Investigate Targeted Advertising
Michelle S. Lam, Ayush Pandit, Colin H. Kalicki, et al.
Proceedings of the ACM on Human-Computer Interaction (2023) Vol. 7, Iss. CSCW2, pp. 1-37
Open Access | Times Cited: 20
AI Regulation Is (not) All You Need
Laura Lucaj, Patrick van der Smagt, Djalel Benbouzid
2022 ACM Conference on Fairness, Accountability, and Transparency (2023), pp. 1267-1279
Closed Access | Times Cited: 18
AI-Driven risk assessment: Revolutionizing audit planning and execution
Ebere Ruth Onwubuariri, Beatrice Oyinkansola Adelakun, Omolara Patricia Olaiya, et al.
Finance & Accounting Research Journal (2024) Vol. 6, Iss. 6, pp. 1069-1090
Open Access | Times Cited: 8
Auditing Flood Vulnerability Geo-Intelligence Workflow for Biases
Brian K. Masinde, Caroline Gevaert, Michael Nagenborg, et al.
ISPRS International Journal of Geo-Information (2024) Vol. 13, Iss. 12, pp. 419-419
Open Access | Times Cited: 8
Making Data Work Count
Srravya Chandhiramowuli, Alex Taylor, Sara Heitlinger, et al.
Proceedings of the ACM on Human-Computer Interaction (2024) Vol. 8, Iss. CSCW1, pp. 1-26
Open Access | Times Cited: 7
From Plane Crashes to Algorithmic Harm: Applicability of Safety Engineering Frameworks for Responsible ML
Shalaleh Rismani, Renee Shelby, Andrew Smart, et al.
(2023), pp. 1-18
Open Access | Times Cited: 16
Certification Labels for Trustworthy AI: Insights From an Empirical Mixed-Method Study
Nicolas Scharowski, Michaela Benk, Swen J. Kühne, et al.
2022 ACM Conference on Fairness, Accountability, and Transparency (2023), pp. 248-260
Open Access | Times Cited: 15
A systematic literature review of Auditing Practices research landscape and future research propositions using bibliometric analysis
Amar Johri, Raj Kumar Singh
Cogent Business & Management (2024) Vol. 11, Iss. 1
Open Access | Times Cited: 5
Achieving a Data-Driven Risk Assessment Methodology for Ethical AI
Anna Felländer, Jonathan Rebane, Stefan Larsson, et al.
Deleted Journal (2022) Vol. 1, Iss. 2
Open Access | Times Cited: 20
Lessons Learned from Assessing Trustworthy AI in Practice
Dennis Vetter, Julia Amann, Frédérick Bruneault, et al.
Deleted Journal (2023) Vol. 2, Iss. 3
Open Access | Times Cited: 13
“Hey SyRI, tell me about algorithmic accountability”: Lessons from a landmark case
Maranke Wieringa
Data & Policy (2023) Vol. 5
Open Access | Times Cited: 11
Participation and Division of Labor in User-Driven Algorithm Audits: How Do Everyday Users Work together to Surface Algorithmic Harms?
Rushi Li, Sara Kingsley, Chelsea Fan, et al.
(2023), pp. 1-19
Open Access | Times Cited: 11
What does it mean to be a responsible AI practitioner: An ontology of roles and skills
Shalaleh Rismani, AJung Moon
(2023), pp. 584-595
Open Access | Times Cited: 11
Make your data fair: A survey of data preprocessing techniques that address biases in data towards fair AI
Amal Tawakuli, Thomas Engel
Journal of Engineering Research (2024)
Open Access | Times Cited: 4
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