CoChat
Sign Up
Cited by 422Ethics and Social Impacts of AIArtificial Intelligence in EducationArtificial Intelligence in Healthcare and Education

The DOI matches its Crossref record; 53 of the 56 references it cites were found in a registry or an index.

Fairness, Accountability, Transparency, and Ethics (FATE) in Artificial Intelligence (AI) and higher education: A systematic review

Open access
Memarian, Bahar, Doleck, Tenzin·2023·Computers and Education: Artificial Intelligence
DOI 10.1016/j.caeai.2023.100152
Open this paper in CoChat — free account

Abstract

The use of Artificial Intelligence or AI is rising in higher education. With this rise, the morality of AI programs is being questioned. There is as such a need to understand how notions of Fairness, Accountability, Transparency, and Ethics or FATE are identified in the AI and higher education studies to date. This systematic review paper aims to understand definitions and studies on FATE and AI in higher education literature. The contribution of this work is to provide a summary of FATE development and the synthesis of the challenges and potentials of each of the reviewed studies. A total of 33 review publications from SCOPUS and Web of Science (WoS) were included in this systematic literature review. We examined definitions of FATE noted in the reviewed articles (may have been multiple in each study) and grouped them into descriptive (understandable by laypeople) and technical (containing jargon) definitions. We also examined the main FATE term studied in detail in each reviewed article and grouped them into qualitative and quantitative studies. Findings show more descriptive definitions exist (especially for fairness) and similarly quantitative definitions mostly emerge for Fairness. Findings also show more quantitative studies exist (especially for fairness) and qualitative definitions mostly emerge for ethics. Generally, though, there are more definitions than relevant studies conducted in the literature. This systematic literature review offers a summary of definitions and studies conducted for FATE terms and AI in higher education literature. Future work may benefit from bridging the gap between laypeople and experts by linking descriptive definitions with technical ones as well as qualitative studies with quantitative ones. Moreover, future work can study accountability and transparency further and make the study of FATE terms more longitudinal, open-access, and reproducible.

Overview

Integrity signals

Registry

Crossref · 10.1016/j.caeai.2023.100152

Cites

53 of 53 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Verification

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

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?

Citations

Cites

Show all 56 references

Research works printed but not linked: 1

  1. Quan, 2023, Vol. 119

Cited by

Sorted by citations

Items 201–300 of 422

Related

See all

Metadata

Authors
Memarian, B., & Doleck, T.
Affiliations
Simon Fraser University (CA)
Corresponding author
Memarian, Bahar
ORCID
Venue
Computers and Education: Artificial Intelligence
Publisher
Elsevier BV
Published
2023
Volume
5
Pages
100152
ISSN
2666-920X (ISSN-L)
DOI
10.1016/j.caeai.2023.100152
OpenAlex
W4382133845
Semantic Scholar
77018f7f53c3c524c88994ea69d528411595b250
Cited by
416
References
56
Citations by year
2026: 138 · 2025: 173 · 2024: 93 · 2023: 12
FWCI
16.27
Type
journal article
Language
en
Open access
Licence
Locations
  • Computers and Education Artificial Intelligence (publishedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • Canada Research Chairs
  • Simon Fraser University
  • Canada Foundation for Innovation
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
  • Social Sciences › Education › Artificial Intelligence in Education
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
Keywords
fairness, accountability, transparency, ethics, artificial intelligence in higher education, AI ethics

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