Three Levels of AI Transparency
Open accessAbstract
The concept of transparency is fragmented in artificial intelligence (AI) research, often limited to transparency of the algorithm alone. We propose that AI transparency operates on three levels—algorithmic, interaction, and social—all of which need to be considered to build trust in AI. We expand upon these levels using current research directions, and identify research gaps resulting from the conceptual fragmentation of AI transparency highlighted within the context of the three levels.
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Another classification establishes transparency at the algorithmic, interaction and social levels, emphasizing the role of the stakeholder audience.
“Another classification establishes transparency at the algorithmic, interaction and social levels [55], emphasizing the role of the stakeholder audience to which the explanation is targeted: developer, designer, owner, user, regulator or society.”
From Connecting the dots in trustworthy Artificial Intelligence: From AI principles, ethics, and key requirements to responsible AI systems and regulation (2023) · cites this paper as [55]
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Díaz-Rodríguez, N., Del Ser, J., Coeckelbergh, M., López de Prado, M., Herrera-Viedma, E., & Herrera, F. (2023) · Information Fusion · doi:10.1016/j.inffus.2023.101896 · Cited by 800 · see citing works
- Authors
- Haresamudram, K., Larsson, S., & Heintz, F.
- Affiliations
- Lund University (SE)
- Linköping University (SE)
- Venue
- Computer
- Published
- 2023-02
- Volume
- 56
- Issue
- 2
- Pages
- 93-100
- ISSN
- 0018-9162 (ISSN-L)
- 1558-0814
- OpenAlex
- W4319879094
- Cited by
- 79
- References
- 5
- Citations by year
- 2026: 24 · 2025: 29 · 2024: 22 · 2023: 4
- FWCI
- 6.99
- Type
- journal article
- Language
- en
- Open access
- green
- lup.lub.lu.se
- Licence
- Locations
- Computer (publishedVersion)
- Lund University Publications (Lund University) (submittedVersion, other-oa)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Physical Sciences › Engineering › Safety, Risk, Reliability and Quality › Safety Systems Engineering in Autonomy
- Keywords
- AI transparency, algorithmic transparency, social transparency, trust in AI, conceptual fragmentation, research gaps
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