Book contents
6 - Can AI Be Transparent?
Published online by Cambridge University Press: 24 July 2026
Summary
This chapter explores the multifaceted nature of AI transparency. First, a general introduction first presented by Haresamudram et al. is given on what transparency entails in AI, distinguishing between algorithmic, interaction, and social transparency. Algorithmic transparency concerns the technical dimension of AI, including explainability techniques and the disclosure of other technical aspects. Interaction transparency allows users to recognize their interactions with AI systems, while social transparency concerns organizational governance structures. In a second step, we outline challenges to transparency, such as technical complexity, proprietary constraints, and potential conflicts with privacy regulations. Subsequently, this chapter provides an overview of the practical application of transparency, followed by real-world examples of transparency initiatives, highlighting both successes and failures. The final section argues that effective transparency requires strong regulatory frameworks, international standards, and AI literacy among stakeholders to ensure meaningful transparency beyond superficial disclosure.
Information
- Type
- Chapter
- Information
- Operationalizing Responsible AI , pp. 110 - 129Publisher: Cambridge University PressPrint publication year: 2026
References
Bell, A., Nov, O., and Stoyanovich, J. 2023. Algorithmic transparency playbook. Center for Responsible AI. https://dataresponsibly.github.io/algorithmic-transparency-playbook/resources/transparency_playbook_camera_ready.pdf (last accessed February 19, 2026).Google Scholar
Bereska, L., and Gavves, E. 2024. Mechanistic interpretability for AI safety: A review. arXiv preprint arXiv:2404.14082.Google Scholar
Bommasani, R., Klyman, K., Kapoor, S., Longpre, S., Xiong, B., Maslej, N., et al. 2024. The foundation model transparency index v1. 1: May 2024. arXiv preprint arXiv:2407.12929.Google Scholar
Chaudhry, M. A., Cukurova, M., and Luckin, R. 2022. A transparency index framework for AI in education. International Conference on Artificial Intelligence in Education (London: Springer), pp. 195–198.Google Scholar
Cheong, B. C. 2024. Transparency and accountability in AI systems: safeguarding wellbeing in the age of algorithmic decision-making. Frontiers in Human Dynamics, 6, 1421273.CrossRefGoogle Scholar
Chmielinski, K., Newman, S., Kranzinger, C. N., Hind, M., Vaughan, J. W., Mitchell, M., et al. 2024. The CLeAR documentation framework for AI transparency: Recommendations for practitioners & context for policymakers. Harvard Kennedy School Shorenstein Center discussion paper. https://shorensteincenter.org/clear-documentation-framework-ai-transparency-recommendations-practitioners-context-policymakers/ (last accessed February 19, 2026).Google Scholar
Constantinides, M., Bogucka, E., Quercia, D., Kallio, S., and Tahaei, M. 2024. RAI guidelines: Method for generating responsible AI guidelines grounded in regulations and usable by (non-) technical roles. Proceedings of the ACM on Human-Computer Interaction, 8(CSCW2), 1–28.Google Scholar
Deshpande, A., and Sharp, H. 2022. Responsible AI systems: Who are the stakeholders? Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society, pp. 227–236. https://doi.org/10.1145/3514094.3534187.CrossRefGoogle Scholar
Eke, C. I., and Shuib, L. 2024. The role of explainability and transparency in fostering trust in AI healthcare systems: A systematic literature review, open issues and potential solutions. Neural Computing and Applications, 37, 1999–2034.CrossRefGoogle Scholar
European Union. 2024 (July 12). Regulation (EU) 2024/1689 of the European Parliament and of the Council of 13 June 2024 laying down harmonised rules on artificial intelligence (Artificial Intelligence Act). Official Journal of the European Union, L 2024/1689.Google Scholar
Felzmann, H., Fosch-Villaronga, E., Lutz, C., and Tamo-Larrieux, A. 2019. Robots and transparency: The multiple dimensions of transparency in the context of robot technologies. IEEE Robotics & Automation Magazine, 26(2), 71–78.CrossRefGoogle Scholar
Felzmann, H., Fosch-Villaronga, E., Lutz, C., and Tamò-Larrieux, A. 2020. Towards transparency by design for artificial intelligence. Science and Engineering Ethics, 26(6), 3333–3361.CrossRefGoogle ScholarPubMed
Ferrara, E. 2023. Fairness and bias in artificial intelligence: A brief survey of sources, impacts, and mitigation strategies. Sci, 6(1), 3.CrossRefGoogle Scholar
Haresamudram, K., Larsson, S., and Heintz, F. 2023. Three levels of AI transparency. Computer, 56(2), 93–100.CrossRefGoogle Scholar
Hassija, V., Chamola, V., Mahapatra, A., Singal, A., Goel, D., Huang, K., et al. 2024. Interpreting black-box models: A review on explainable artificial intelligence. Cognitive Computation, 16(1), 45–74.CrossRefGoogle Scholar
Hick, A., and Ziefle, M. 2022. A qualitative approach to the public perception of AI. International Journal on Cybernetics & Informatics (IJCI), 11(11), 1.CrossRefGoogle Scholar
Hornberger, M., Bewersdorff, A., and Nerdel, C. 2023. What do university students know about Artificial Intelligence? Development and validation of an AI literacy test. Computers and Education: Artificial Intelligence, 5, 100165.Google Scholar
Kucklick, J.-P., and Müller, O. 2023. Tackling the accuracy-interpretability trade-off: Interpretable deep learning models for satellite image-based real estate appraisal. ACM Transactions on Management Information Systems, 14(1), 1–24.CrossRefGoogle Scholar
Langenkamp, M., Costa, A., and Cheung, C. 2020. Hiring fairly in the age of algorithms. arXiv preprint arXiv:2004.07132.CrossRefGoogle Scholar
Lao, N. 2020. Reorienting machine learning education towards tinkerers and ML-engaged citizens. PhD Thesis, Massachusetts Institute of Technology Cambridge.Google Scholar
Liu, B. 2021. In AI we trust? Effects of agency locus and transparency on uncertainty reduction in human–AI interaction. Journal of Computer-Mediated Communication, 26(6), 384–402.CrossRefGoogle Scholar
Longo, L., Brcic, M., Cabitza, F., Choi, J., Confalonieri, R., Del Ser, J., et al. 2024. Explainable Artificial Intelligence (XAI) 2.0: A manifesto of open challenges and interdisciplinary research directions. Information Fusion, 106, 102301.CrossRefGoogle Scholar
Lund, B., Orhan, Z., Mannuru, N. R., Bevara, R. V. K., Porter, B., Vinaih, M. K., et al. 2025. Standards, frameworks, and legislation for artificial intelligence (AI) transparency. AI and Ethics, 5, 3639–3655.CrossRefGoogle Scholar
Lundberg, S. M., and Lee, S.-I. 2017. A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 30.Google Scholar
Matthias, A. 2004. The responsibility gap: Ascribing responsibility for the actions of learning automata. Ethics and Information Technology, 6, 175–183.CrossRefGoogle Scholar
Mena, L. J., García, V., Félix, V. G., Ostos, R., Martínez-Peláez, R., Ochoa-Brust, A., et al. 2024. Enhancing financial risk prediction with symbolic classifiers: Addressing class imbalance and the accuracy–interpretability trade-off. Humanities and Social Sciences Communications, 11(1), 1–11.CrossRefGoogle Scholar
Mitchell, M., Wu, S., Zaldivar, A., Barnes, P., Vasserman, L., Hutchinson, B., et al. 2019. Model cards for model reporting. Proceedings of the Conference on Fairness, Accountability, and Transparency, pp. 220–229. https://doi.org/10.1145/3287560.3287596.CrossRefGoogle Scholar
Mylly, U.-M. 2023. Transparent AI? Navigating between rules on trade secrets and access to information. IIC-International Review of Intellectual Property and Competition Law, 54(7), 1013–1043.Google Scholar
Novelli, C., Taddeo, M., and Floridi, L. 2024. Accountability in artificial intelligence: What it is and how it works. AI & Society, 39(4), 1871–1882.CrossRefGoogle Scholar
Pagallo, U. 2023. Why the AI act won’t trigger a Brussels effect. AI Approaches to the Complexity of Legal Systems (London: Springer).Google Scholar
Rao, P. S. B., Šćepanović, S., Zhou, K., Bogucka, E. P., and Quercia, D. 2025. RiskRAG: A data-driven solution for improved AI model risk reporting. Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems, pp. 1–26. https://doi.org/10.1145/3706598.3713979.CrossRefGoogle Scholar
Ribeiro, M. T., Singh, S., and Guestrin, C. 2016. “Why should I trust you?” Explaining the predictions of any classifier. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1135–1144. https://doi.org/10.1145/2939672.2939778.CrossRefGoogle Scholar
Sandeen, S. K., and Aplin, T. 2022. Trade secrecy, factual secrecy and the hype surrounding AI. In Abbott, R. (ed.), Research Handbook on Intellectual Property and Artificial Intelligence (Cheltenham: Edward Elgar Publishing), pp. 443–460.Google Scholar
Touretzky, D., Martin, F., Seehorn, D., Breazeal, C., and Posner, T. 2019. Special Session: AI for K-12 Guidelines Initiative. Proceedings of the 50th ACM Technical Symposium on Computer Science Education. SIGCSE ’19 (New York: Association for Computing Machinery), pp. 492–493.CrossRefGoogle Scholar
Walmsley, J. 2021. Artificial intelligence and the value of transparency. AI & Society, 36(2), 585–595.CrossRefGoogle Scholar
Wang, Z., Sun, L., and Zhu, H. 2020. Defining social engineering in cybersecurity. IEEE Access, 8, 85094–85115.CrossRefGoogle Scholar
The White House. 2023. Safe, secure, and trustworthy development and use of artificial intelligence. www.federalregister.gov/documents/2023/11/01/2023-24283/safe-secure-and-trustworthy-development-and-use-of-artificial-intelligence (last accessed March 12, 2025).Google Scholar
Wright, L., Muenster, R. M., Vecchione, B., Qu, T., Cai, P., Smith, A., et al. 2024. Null compliance: NYC Local Law 144 and the challenges of algorithm accountability. Proceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency, pp. 1701–1713. https://doi.org/10.1145/3630106.3658998.CrossRefGoogle Scholar
Zhao, J., Wang, T., Yatskar, M., Ordonez, V., and Chang, K.-W. 2017. Men also like shopping: Reducing gender bias amplification using corpus-level constraints. arXiv preprint arXiv:1707.09457.Google Scholar
Accessibility standard: WCAG 2.2 AAA
Why this information is here
This section outlines the accessibility features of this content - including support for screen readers, full keyboard navigation and high-contrast display options. This may not be relevant for you.Accessibility Information
The HTML of this chapter complies with version 2.2 of the
Web Content Accessibility Guidelines
(WCAG), offering more comprehensive accessibility measures for a broad range
of users and attains the highest
(AAA)
level of WCAG compliance, optimising the user experience by meeting the most extensive
accessibility guidelines.
Content Navigation
Table of contents navigation
Allows you to navigate directly to chapters, sections, or non‐text items through a linked table of contents, reducing the need for extensive scrolling.
Allows you to navigate directly to chapters, sections, or non‐text items through a linked table of contents, reducing the need for extensive scrolling.
Index navigation
Provides an interactive index, letting you go straight to where a term or subject appears in the text without manual searching.
Provides an interactive index, letting you go straight to where a term or subject appears in the text without manual searching.
Reading Order & Textual Equivalents
Single logical reading order
You will encounter all content (including footnotes, captions, etc.) in a clear, sequential flow, making it easier to follow with assistive tools like screen readers.
You will encounter all content (including footnotes, captions, etc.) in a clear, sequential flow, making it easier to follow with assistive tools like screen readers.
Short alternative textual descriptions
You get concise descriptions (for images, charts, or media clips), ensuring you do not miss crucial information when visual or audio elements are not accessible.
You get concise descriptions (for images, charts, or media clips), ensuring you do not miss crucial information when visual or audio elements are not accessible.
Full alternative textual descriptions
You get more than just short alt text: you have comprehensive text equivalents, transcripts, captions, or audio descriptions for substantial non‐text content, which is especially helpful for complex visuals or multimedia.
You get more than just short alt text: you have comprehensive text equivalents, transcripts, captions, or audio descriptions for substantial non‐text content, which is especially helpful for complex visuals or multimedia.
Visualised data also available as non-graphical data
You can access graphs or charts in a text or tabular format, so you are not excluded if you cannot process visual displays.
You can access graphs or charts in a text or tabular format, so you are not excluded if you cannot process visual displays.
Visual Accessibility
Use of high contrast between text and background colour
You benefit from high‐contrast text, which improves legibility if you have low vision or if you are reading in less‐than‐ideal lighting conditions.
You benefit from high‐contrast text, which improves legibility if you have low vision or if you are reading in less‐than‐ideal lighting conditions.