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 accessAbstract
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.
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“Responsible use may promote ethical reflection and conscientious academic behavior (Memarian & Doleck, 2023), yet uncritical reliance can undermine originality, authorship norms, and institutional trust (Chan, 2023;Eaton, 2022;Fowler, 2023;Grassini, 2023;Katsamakas et al., 2024;Malinka et al., 2023;Nazaretsky et al., 2025).”
From Academic Integrity and Students’ Ethical Use of ChatGPT in Higher Education (2026) · cites this paper as Memarian & Doleck, 2023
In the citing paper
“Rights-aware educational research establishes the relevance of learner choice, academic values, legal literacy, and context-specific governance(Berendt et al., 2020;Lukianova et al., 2025;Memarian & Doleck, 2023;Oncioiu & Bularca, 2025;Panagopoulou et al., 2025;Temper et al., 2025).”
From Responsible AI in Polish Higher-Education Administration under the EU AI Act: High-Risk Use Cases, Fundamental-Rights Impact Assessment, and Institutional Accountability (2026) · cites this paper as Memarian & Doleck, 2023
In the citing paper
“The disparities in the access to high-quality tools, digital literacy, and linguistic fluency may be interpreted as digital, cultural, and linguistic capital, which benefits some groups and disfavors others (Memarian and Doleck, 2023).”
From Mapping the impact of generative AI in higher education: A scoping review of psychological and equity dimensions. (2026) · cites this paper as Memarian and Doleck, 2023
In the citing paper
“This can result from biased data, faults in the design of the model used, or societal inequalities where inbuilt into training data sets.”
From A decision-support framework for evaluating AI-enabled ESG strategies in the context of sustainable manufacturing systems (2025) · cites this paper as [40]
In the citing paper
“In such a situation, the successes of bias mitigation efforts can be held as an essential metric toward measuring how well an organization aligns its applications of AI with ethical standards38–40.”
From A decision-support framework for evaluating AI-enabled ESG strategies in the context of sustainable manufacturing systems (2025) · cites this paper as [40]
In the citing paper
“Bias is a key ethical issue for AI - sometimes, the outcome of a computation can have an unacceptably discriminatory bias.”
From A decision-support framework for evaluating AI-enabled ESG strategies in the context of sustainable manufacturing systems (2025) · cites this paper as [40]
In the citing paper
“Artificial intelligence fairness is applied to education technology to ensure equal opportunity for students regardless of their background [273].”
From AI Fairness in Data Management and Analytics: A Review on Challenges, Methodologies and Applications (2023) · cites this paper as [273]
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The Ethics of ChatGPT-Exploring the Ethical Issues of an Emerging Technology
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“Researchers also note challenges such as the varied level of technological readiness among teachers and students as well as concerns regarding accuracy, biases, accountability, transparency, and ethics (e.g., Kohnke et al., 2023; Memarian & Doleck, 2023; Ranalli, 2021).”
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Metadata
- Authors
- Memarian, B., & Doleck, T.
- Affiliations
- Simon Fraser University (CA)
- Corresponding author
- Memarian, Bahar
- Venue
- Computers and Education: Artificial Intelligence
- Publisher
- Elsevier BV
- Published
- 2023
- Volume
- 5
- Pages
- 100152
- ISSN
- 2666-920X (ISSN-L)
- 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
- gold
- doi.org
- 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
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