Accountability in Algorithmic Decision-making: A view from computational journalism
Abstract
Every fiscal quarter automated writing algorithms churn out thousands of corporate earnings articles for the AP (Associated Press) based on little more than structured data. Companies such as Automated Insights, which produces the articles for AP, and Narrative Science can now write straight news articles in almost any domain that has clean and well-structured data: finance, sure, but also sports, weather, and education, among others. The articles aren’t cardboard either; they have variability, tone, and style, and in some cases readers even have difficulty distinguishing the machine-produced articles from human-written ones.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
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?
The Freedom of Information Act (FOIA, 1967) protects trade secrets, allowing third party companies to still obtain personal data.
“the Freedom of Information Act (FOIA, 1967) protects trade secrets, allowing third party companies to still obtain personal data (Diakopoulos, 2015)”
From Expanding on the Frames: Making a Case for Algorithmic Literacy (2023) · cites this paper as Diakopoulos, 2015
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Letham, B., Rudin, C., McCormick, T. H., & Madigan, D. (2015) · The Annals of Applied Statistics · doi:10.1214/15-aoas848 · Cited by 766 · see citing works
Accountability in a computerized society
Nissenbaum, H. (1996) · Science and Engineering Ethics · doi:10.1007/bf02639315 · Cited by 346 · see citing works
Flagged first, then by citations
Research works printed but not linked: 10
- ACM. 2015. Software Engineering Code of Ethics and Professional Practice; https://www.acm.org/about/ se-code#full.
- Citron, D., Pasquale, F. 2014. The scored society: due process for automated predictions. Washington Law Review 89.
- Clerwall, C. 2014. Enter the robot journalist. Journalism Practice 8(5): 519-531.
- Diakopoulos, N. 2015. Algorithmic accountability: journalistic investigation of computational power structures. Digital Journalism 3(3): 398-415.
- Diakopoulos, N. 2014. Algorithmic defamation: the case through the lens of technology, surveillance, and privacy. Ohio State Law Journal 74.
- Kashin, K., et al. 2015. Systematic bias and nontransparency in US Social Security Administration forecasts. Journal of Economic Perspectives 29(2).
- Kraemer, F., et al. 2010. Is there an ethics of algorithms? Ethics and Information Technology 13(3): 251-260.
- Letham, B., et al. 2015. Building interpretable classifiers with rules using Bayesian analysis. Annals of Applied Statistics.
- Mühlbacher, T., et al. 2014. Opening the black box: strategies for increased user involvement in existing algorithm implementations. IEEE Transactions on Visualization and Computer Graphics 20(12): 1643-1652.
- Schaffer, J., et al. 2015. Getting the message?: a study of explanation interfaces for microblog data analysis. Proceedings of the 20 th International Conference on Intelligent User Interfaces (IUI): 345-356.
Cited by
Sorted by citations
Towards transparency by design for artificial intelligence
Felzmann, H., Fosch-Villaronga, E., Lutz, C., & Tamò-Larrieux, A. (2020) · Science and Engineering Ethics · doi:10.1007/s11948-020-00276-4 · Cited by 588 · see citing works
Wieringa, M. (2020) · Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency · doi:10.1145/3351095.3372833 · Cited by 321 · see citing works
Decision Provenance: Harnessing Data Flow for Accountable Systems
Singh, J., Cobbe, J., & Norval, C. (2019) · IEEE Access · doi:10.1109/access.2018.2887201 · Cited by 110 · see citing works
Unveiling the Black Box: Bringing Algorithmic Transparency to AI
Chaudhary, G. (2024) · Masaryk University Journal of Law and Technology · doi:10.5817/mujlt2024-1-4 · Cited by 74 · see citing works
Accountability in Government Use of <scp>AI</scp> : Citizen Concerns and Preferences
Feng, N., & Chandra, Y. (2025) · Public Administration · doi:10.1111/padm.70030 · Cited by 18 · see citing works
Expanding on the Frames: Making a Case for Algorithmic Literacy
Loyola Marymount University, & Archambault, S. (2023) · Communications in Information Literacy · doi:10.15760/comminfolit.2023.17.2.11 · Cited by 19 · see citing works
- Authors
- Diakopoulos, N.
- Affiliations
- University of Maryland, College Park
- Venue
- Queue
- Published
- 2015-11
- Volume
- 13
- Issue
- 9
- Pages
- 126-149
- ISSN
- 1542-7730
- 1542-7749
- Cited by
- 13
- References
- 2
- Language
- en
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 ›
Are you an author, or do you see a problem with this page? Request a review
