Algorithmic bias: should students pay the price?
Open accessThe 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?
In the UK in 2020, an automated assessment system disproportionately penalized students from schools in low-income communities, lowering their grades compared to those from institutions with a track record of high performance.
“However, the system was found to disproportionately penalize students from schools located in low-income communities, lowering their grades compared to those from institutions with a track record of high performance.”
From Ethical and regulatory challenges of Generative AI in education: a systematic review (2025) · cites this paper as Smith, 2020
The paper’s main text (page unavailable) says the UK’s algorithmic grading disproportionately downgraded students from disadvantaged backgrounds and favoured those attending private or independent schools. It does not identify the favoured schools as institutions with a track record of high performance, or specifically describe the affected schools as located in low-income communities. (LLM, conf=0.86)
Experimental — accuracy not yet measuredThe use of AI in education, if not designed with equity in mind, can reinforce structural inequalities rather than mitigate them.
“This case showed that the use of AI in education, if not designed with equity in mind, can reinforce structural inequalities rather than mitigate them (Smith, 2020).”
From Ethical and regulatory challenges of Generative AI in education: a systematic review (2025) · cites this paper as Smith, 2020
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
Still being checked.
Cited by
Sorted by citations
Artificial intelligence in education: Addressing ethical challenges in K-12 settings
Akgun, S., & Greenhow, C. (2022) · AI and Ethics · doi:10.1007/s43681-021-00096-7 · Cited by 1,473 · see citing works
Baker, R. S., & Hawn, A. (2022) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-021-00285-9 · Cited by 993 · see citing works
Ethical and regulatory challenges of Generative AI in education: a systematic review
García-López, I. M., & Trujillo-Liñán, L. (2025) · Frontiers in Education · doi:10.3389/feduc.2025.1565938 · Cited by 144 · see citing works
“For example, while previous studies have explored issues such as algorithmic biases (Baker and Hawn, 2022; Smith, 2020) or automated feedback (Bauer et al., 2023), this analysis highlights the need for an interdisciplinary approach that combines legal governance, digital literacy and clear ethical…”
Humans perceive warmth and competence in artificial intelligence
McKee, K. R., Bai, X., & Fiske, S. T. (2023) · iScience · doi:10.1016/j.isci.2023.107256 · Cited by 97 · see citing works
Wang, D., Bian, C., & Chen, G. (2024) · British Journal of Educational Technology · doi:10.1111/bjet.13466 · Cited by 70 · see citing works
Trust or mistrust in algorithmic grading? An embedded agency perspective
Jackson, S., & Panteli, N. (2022) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2022.102555 · Cited by 40 · see citing works
Pennant, A.-L. M. O. O. (2022) · Educational Review · doi:10.1080/00131911.2021.2023102 · Cited by 33 · see citing works
Fairness for machine learning software in education: A systematic mapping study
Pham, N., Ngoc, H. P., & Nguyen‐Duc, A. (2024) · Journal of Systems and Software · doi:10.1016/j.jss.2024.112244 · Cited by 42 · see citing works
Chun, Z., Ning, Y., Chen, J., & Wijaya, T. T. (2025) · Education and Information Technologies · doi:10.1007/s10639-025-13617-2 · Cited by 38 · see citing works
Day, E., Pothong, K., Atabey, A., & Livingstone, S. (2022) · Learning Media and Technology · doi:10.1080/17439884.2022.2152838 · Cited by 30 · see citing works
Reimagining the machine learning life cycle to improve educational outcomes of students
Liu, L. T., Wang, S., Britton, T., & Abebe, R. (2023) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2204781120 · Cited by 36 · see citing works
Lünich, M., Keller, B., & Marcinkowski, F. (2024) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2024.100305 · Cited by 30 · see citing works
Ethical use of artificial intelligence and new technologies in Education 5.0
Smyrnaiou, Z., Liapakis, A., & Bougia, A. (2023) · Journal of Artificial Intelligence, Machine Learning and Data Science · doi:10.51219/jaimld/anastasios-liapakis/15 · Cited by 22 · see citing works
Monroe‐White, T. (2021) · ACM SIGCPR Conference on Computer Personnel Research (SIGCPR) · doi:10.1145/3458026.3462161 · Cited by 20 · see citing works
Heaton, D., Nichele, E., Clos, J., & Fischer, J. E. (2023) · PLoS ONE · doi:10.1371/journal.pone.0288662 · Cited by 19 · see citing works
Humans perceive warmth and competence in artificial intelligencePreprint
McKee, K. R., Bai, X., & Fiske, S. T. (2021) · doi:10.31234/osf.io/5ursp · Cited by 18 · see citing works
Lünich, M., Keller, B., & Marcinkowski, F. (2023) · Technology Knowledge and Learning · doi:10.1007/s10758-023-09698-y · Cited by 21 · see citing works
ARTIFICIAL INTELLIGENCE (AI) BIAS IMPACTS: CLASSIFICATION FRAMEWORK FOR EFFECTIVE MITIGATION
(2023) · Issues in Information Systems · doi:10.48009/4_iis_2023_128 · Cited by 20 · see citing works
Heaton, D., Clos, J., Nichele, E., & Fischer, J. E. (2023) · PeerJ Computer Science · doi:10.7717/peerj-cs.1211 · Cited by 12 · see citing works
Assessing responsible innovation training
Stahl, B. C., Aicardi, C., Brooks, L., Craigon, P. J., Cunden, M., Burton, S. D., Heaver, M. D., de Saille, S., Dolby, S., Dowthwaite, L., et al. (2023) · Journal of Responsible Technology · doi:10.1016/j.jrt.2023.100063 · Cited by 8 · see citing works
Parameter-Efficiently Fine-Tuning Large Language Models for Classroom Dialogue Analysis
Wang, D., Zheng, Y., Li, J., & Chen, G. (2025) · IEEE Transactions on Learning Technologies · doi:10.1109/tlt.2025.3567995 · Cited by 9 · see citing works
Comprehending Algorithmic Bias and Strategies for Fostering Trust in Artificial Intelligence
Sidhi Menon, U., Siby, T., & Natchimuthu, N. (2024) · Advances in web technologies and engineering book series · doi:10.4018/979-8-3693-1762-4.ch014 · Cited by 7 · see citing works
Yasmin, M., Fatima, W., & Irshad, I. (2025) · Sustainable Futures · doi:10.1016/j.sftr.2025.100809 · Cited by 6 · see citing works
Being a data professional: give voice to value in a data driven society
Gotterbarn, D., & Kreps, D. (2020) · AI and Ethics · doi:10.1007/s43681-020-00027-y · Cited by 6 · see citing works
Gorgun, G., & Yildirim‐Erbasli, S. N. (2024) · Journal of Educational Measurement · doi:10.1111/jedm.12420 · Cited by 6 · see citing works
Kuleto, V., Mihoreanu, L., Dinu, D. G., Ilić, M. P., & Păun, D. G. (2023) · Springer series on cultural computing · doi:10.1007/978-3-031-27166-3_7 · Cited by 4 · see citing works
Hillman, V., Manolev, J., Johnston, S.-K., Kumar, P. C., Martin, F., Zeide, E., Vladova, G., & Lai, R. (2024) · Springer eBooks · doi:10.1007/978-3-031-69362-5_76 · Cited by 3 · see citing works
Normalizing doubt: AI, democratic confidence, and the OECD trust framework
Smith, C. (2025) · AI & Society · doi:10.1007/s00146-025-02789-7 · Cited by 3 · see citing works
Human-centered generative AI in education: ethical challenges and equity-driven solutions
Abu, S., Uddın, M. M., Moon, J., Awoyemi, I. D., Baah-Mintah, R., & Ghooreian, A. (2026) · Ethics & Behavior · doi:10.1080/10508422.2026.2685014 · Cited by 4 · see citing works
ARTIFICIAL INTELLIGENCE (AI) BIAS IMPACTS: CLASSIFICATION FRAMEWORK FOR EFFECTIVE MITIGATION
(2023) · Issues in Information Systems · doi:10.48009/4_iis_2023_129 · Cited by 4 · see citing works
O algoritmo como materialidade discursiva em um contexto de educação linguística
Hashiguti, S. T., & Fagundes, I. Z. Z. (2023) · Letras & letras · doi:10.14393/ll63-v38-2022-27 · Cited by 3 · see citing works
Choi, J., Karumbaiah, S., & Matayoshi, J. (2025) · International Conference on Learning Analytics and Knowledge (LAK) · doi:10.1145/3706468.3706540 · Cited by 2 · see citing works
Students' Privacy and Ethical Concerns of Using Social Virtual Worlds for Online Learning
Bailey, J. O., You, X., Won, A. S., Ahn, S. J., & MacIntyre, B. (2025) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3757428 · Cited by 2 · see citing works
Developing judgement for business: an AI-based model of independent management learning
Johnson, M. W., Maitland, E., & Sofka, W. (2025) · Journal of Business Research · doi:10.1016/j.jbusres.2025.115842 · Cited by 9 · see citing works
Exploring Education Techno-Monitoring Futures: A Participatory Speculative Critical Design Case
Durall, E. (2021) · Interaction design & architecture(s)/ID&A Interaction design & architecture(s) · doi:10.55612/s-5002-051-009 · Cited by 2 · see citing works
The guts of assessment: a digital architecture for machine learning and analogue judgement
Johnson, M. W., & Saleh, R. (2022) · Interactive Learning Environments · doi:10.1080/10494820.2022.2135105 · Cited by 2 · see citing works
Do we Reach Desired Disparate Impact with In-Processing Fairness Techniques?
Radovanović, S., Delibašić, B., & Suknović, M. (2022) · Procedia Computer Science · doi:10.1016/j.procs.2022.11.173 · Cited by 1 · see citing works
Countering flaws in algorithm design and applications: a Delphi study
Gokhale, A. A. (2024) · AI & Society · doi:10.1007/s00146-024-02124-6 · Cited by 1 · see citing works
Cearns, J., & Knox, H. (2024) · The Cambridge Journal of Anthropology · doi:10.3167/cja.2024.420103 · Cited by 2 · see citing works
Navigating the Ethical Frontier
Öztürk, H. İ., Doğuş, M., Şahin, V., Çakmak, M., & Gürses, İ. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-0122-8.ch011 · Cited by 1 · see citing works
Will You Have Autonomy in the Metaverse?Preprint
Geddes, K. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4524680 · Cited by 1 · see citing works
Hur, P., & Kubsch, M. (2025) · Wiley eBooks · doi:10.1002/9781394352821.ch15 · Cited by 1 · see citing works
(2023) · PeerJ Computer Science · doi:10.7287/peerj-cs.1211v0.1/reviews/2 · Cited by 1 · see citing works
Eren, S. U. N. A., & Özer, M. (2025) · Chinese/English Journal of Educational Measurement and Evaluation · doi:10.59863/cmge2179
Fairness Testing: Review of the Relevance and Methodologies of Ensuring Software Fairness
Zia, I., Raj, A., & Dabu, J. B. (2026) · Communications in computer and information science · doi:10.1007/978-3-032-08977-9_48
Beyond the Basics: Unravelling AI Education Policies and Practices in Asia–Pacific
Jha, N. K., & Bhagat, K. K. (2026) · Springer eBooks · doi:10.1007/978-3-032-12174-5_3
Sevhake, M., & Hofisi, C. (2026) · AI in Education · doi:10.3390/aieduc2030024 · Cited by 1 · see citing works
Mandalunes, J. A. (2026) · Research Square · doi:10.21203/rs.3.rs-10621652/v1
Tjandra, D. S. (2026) · Verbum et Ecclesia · doi:10.4102/ve.v47i1.3863
- Authors
- Smith, H.
- Affiliations
- University of Bristol (GB)
- Corresponding author
- Smith, Helen
- ORCID
- Smith, Helen
- Venue
- AI & SOCIETY
- Published
- 2020-09-12
- Volume
- 35
- Issue
- 4
- Pages
- 1077-1078
- ISSN
- 0951-5666 (ISSN-L)
- 1435-5655
- PMC
- PMC7486588
- OpenAlex
- W3085768849
- Cited by
- 52
- References
- 0
- Citations by year
- 2026: 6 · 2025: 11 · 2024: 8 · 2023: 16 · 2022: 5 · 2021: 5 · 2020: 1
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- AI & Society (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Explore Bristol Research (acceptedVersion, other-oa)
- Bristol Research (University of Bristol) (submittedVersion, other-oa)
- Topics
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
- Keywords
- algorithmic bias, algorithmic decision-making, student fees, higher education, algorithmic fairness, automated decision systems
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
