Transparency in artificial intelligence
Open accessAbstract
Introduction: transparency in AI Transparency is indeed a multifaceted concept used by various disciplines (Margetts, 2011; Hood, 2006). Recently, it has gone through a resurgence with regards to contemporary discourses around artificial intelligence (AI).
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Transparency has emerged as a foundational principle in ethical AI discourse, particularly within education, where disclosure practices directly affect academic credibility.
“Transparency has emerged as a foundational principle in ethical AI discourse, particularly within education, where disclosure practices directly affect academic credibility.”
From Academic Integrity and Students’ Ethical Use of ChatGPT in Higher Education (2026) · cites this paper as Larsson & Heintz, 2020
The Introduction (listed under Abstract; no page provided) describes transparency as a prominent principle in ethical AI guidelines, supporting the first part of the claim. The cited passages do not address education or establish that disclosure practices directly affect academic credibility; searches for those terms found no supporting passage. (LLM, conf=0.88)
Experimental — accuracy not yet measuredData ethics emphasized the need for transparency in data usage in AIED.
“Data ethics emphasized the need for transparency in data usage in AIED (Larsson & Heintz, 2020).”
From Ethical principles for artificial intelligence in education (2023) · cites this paper as Larsson & Heintz, 2020
The Introduction (listed under Abstract; no page given) identifies transparency as a key principle in AI ethics guidelines. Section 2, “A multidisciplinary notion of transparency” (no page given), links AI transparency to the origins of data and how personal data travels. The paper does not address data usage specifically in artificial intelligence in education (AIED), so the educational setting is unsupported. (LLM, conf=0.86)
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Cites
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“In fact, “transparency” is the single most common, and one of the key five principles emphasised in the vast number – a recent study counted 84 – of ethical guidelines addressing AI on a global level (Jobin et al., 2019).”
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“…of disclosure” – affecting everything from trending topics on Twitter to security issues and welfare distribution. data and algorithm user literacy, indicating that ordinary users’ basic understanding has a3. direct effect on transparency in applied AI (Burrell, 2016; Haider & Sundin, 2019).”
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“On the one hand, AI indeed is a contested concept that lacks clear consensus, both in computer science (Monett, Lewis & Thórisson, 2020), law (Martinez, 2019) and the public perception (Fast & Horvitz, 2017).”
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“However, and as noted by Mittelstadt, Russell and Wachter (2019), explanations of machine learning models and predictions can serve many functions and audiences: explanations can be necessary to comply with relevant legislation (Doshi-Velez et al., 2017), verify and improve the functionality of a system, and arguably…”
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Transparent to whom? No algorithmic accountability without a critical audience
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“…to data protection, there are calls for more studies on how administrative law should adapt to more automated forms of decision-making (e.g., Cobbe, 2019; see also Oswald’s (2018) review of a number of long-standing rules in English administrative law designed to regulate the discretionary…”
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“Citation for published version (APA): Larsson, S., & Heintz, F. (2020). Transparency in artificial intelligence.”
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Research works printed but not linked: 1
- [1]1. See for example the ACM Conference on Fairness, Accountability, and Transparency (ACM FAccT), https://fatconference.org
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“Data ethics emphasized the need for transparency in data usage in AIED (Larsson & Heintz, 2020 ).”
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Barriers to and facilitators of artificial intelligence adoption in health care: scoping review
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On the Governance of Artificial Intelligence through Ethics Guidelines
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Self-citationAuditing of AI: Legal, Ethical and Technical Approaches
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“For example, research has suggested that auditing contributes to good governance through procedural regularity and transparency (Floridi, 2017b; Larsson, 2020; Loi et al., 2020) and prevents harm by ensuring proactivity in the design of AI systems (Kazim & Koshiyama, 2020).”
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Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback
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“For all of these reasons, working to incorporate transparency standards into an AI governance framework will be important [Larsson and Heintz, 2020, Anderljung et al., 2023].”
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Academic Integrity and Students’ Ethical Use of ChatGPT in Higher Education
Shishakly, R., & Nachouki, M. (2026) · Journal of Information Technology Education: Research · doi:10.28945/5730 · Cited by 4 · see citing works
“Early governance literature conceptualizes transparency as a multidimensional and system - level construct rather than a narrow technical property (Hood, 2006; Larsson & Heintz, 2020; Margetts, 2011; Miller, 2019; Theodorou et al., 2017).”
- Authors
- Larsson, S., & Heintz, F.
- Affiliations
- Linköping University (SE)
- Corresponding author
- Larsson, Stefan
- Venue
- Internet Policy Review
- Published
- 2020-05-05
- Volume
- 9
- Issue
- 2
- ISSN
- 2197-6775 (ISSN-L)
- OpenAlex
- W3015977534
- Cited by
- 458
- References
- 18
- Citations by year
- 2026: 88 · 2025: 90 · 2024: 94 · 2023: 108 · 2022: 55 · 2021: 18 · 2020: 5
- FWCI
- 63.38
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- cc-by
- Locations
- Internet Policy Review (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Lund University Publications (Lund University) (submittedVersion, other-oa)
- Topics
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Social Sciences › Law › Digitalization, Law, and Regulation
- Keywords
- artificial intelligence, transparency, algorithmic transparency, AI governance, public understanding of AI
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