Governing Public–Private Artificial Intelligence Partnerships: Balancing Public Value and Commercial Interests in the Digital State
Article Sidebar
Main Article Content
Abstract
This study examines the governance of Public–Private Partnerships (PPPs) in Artificial Intelligence (AI) within the context of the digital state. As governments increasingly rely on AI-driven systems for public service delivery, risk assessment, and administrative decision-making, collaboration with private technology firms has moved beyond conventional outsourcing arrangements into the core architecture of public authority. Unlike traditional PPPs focused on infrastructure or service provision, AI-enabled partnerships embed private actors within algorithmic decision infrastructures that directly shape public outcomes. Drawing on qualitative policy analysis and documentary research, this study integrates PPP governance, algorithmic governance, and public value theory. The findings demonstrate that AI-driven partnerships reconfigure PPPs from contractual risk allocation toward the co-production of algorithmic authority. Data asymmetry, technical opacity, and commercial incentives generate structural tensions between market efficiency and the preservation of public value, including transparency, accountability, equity, and legitimacy. The study proposes an integrated governance framework encompassing four dimensions: contract governance, data governance, algorithmic transparency and auditability, and public value accountability. By conceptualizing Public–Private AI Partnerships as a form of co-production of algorithmic authority, this research extends PPP theory into the domain of digital governance and highlights the institutional conditions necessary to sustain democratic legitimacy in AI-enabled public administration.
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
License Terms
All articles published in this journal are licensed under the Creative Commons Attribution–NonCommercial–NoDerivatives 4.0 International License (CC BY-NC-ND 4.0).
Under this license, users are permitted to:
- Share — copy and redistribute the material in any medium or format.
Under the following conditions:
- Attribution (BY)
Users must give appropriate credit to the author(s) and the journal, provide a link to the license, and indicate if any changes were made. Attribution must not suggest endorsement by the author(s) or the journal.
- NonCommercial (NC)
The material may not be used for commercial purposes. Any use primarily intended for or directed toward commercial advantage or monetary compensation is prohibited without prior written permission from the copyright holder.
- NoDerivatives (ND)
If users remix, transform, translate, adapt, or build upon the material, they may not distribute the modified material. The work must be shared in its original and unaltered form.
This license ensures that published works remain freely accessible to the academic community and the public, while protecting the integrity of the original research and the rights of the author(s).
For the full legal code of this license, please visit:
https://creativecommons.org/licenses/by-nc-nd/4.0/
References
Bovens, M. (2007). Analysing and assessing accountability: A conceptual framework. European Law Journal, 13(4), 447–468. https://doi.org/10.1111/j.1468-0386.2007.00378.x
Bryson, J. M., Crosby, B. C., & Bloomberg, L. L. (2014). Public value governance: Moving beyond traditional public administration and the New Public Management. Public Administration Review, 74(4), 445–456. https://doi.org/10.1111/puar.12238
Dunleavy, P., Margetts, H., Bastow, S., & Tinkler, J. (2006). New public management is dead—Long live digital-era governance. Journal of Public Administration Research and Theory, 16(3), 467–494. https://doi.org/10.1093/jopart/mui057
Eubanks, V. (2018). Automating inequality: How high-tech tools profile, police, and punish the poor. St. Martin’s Press.
Hodge, G. A., & Greve, C. (2007). Public–private partnerships: An international performance review. Public Administration Review, 67(3), 545–558. https://doi.org/10.1111/j.1540-6210.2007.00736.x
Hood, C. (1991). A public management for all seasons? Public Administration, 69(1), 3–19. https://doi.org/10.1111/j.1467-9299.1991.tb00779.x
Kitchin, R. (2017). Thinking critically about and researching algorithms. Information, Communication & Society, 20(1), 14–29. https://doi.org/10.1080/1369118X.2016.1154087
Klijn, E.-H., & Teisman, G. R. (2003). Institutional and strategic barriers to public–private partnership: An analysis of Dutch cases. Public Money & Management, 23(3), 137–146. https://doi.org/10.1111/1467-9302.00361
Margetts, H., & Dorobantu, C. (2019). Rethink government with AI. Nature, 568(7751), 163–165. https://doi.org/10.1038/d41586-019-01099-5
Moore, M. H. (1995). Creating public value: Strategic management in government. Harvard University Press.
OECD. (2019). OECD principles on artificial intelligence. OECD Publishing. https://doi.org/10.1787/eedfee77-en
Pasquale, F. (2015). The black box society: The secret algorithms that control money and information. Harvard University Press.
Yeung, K. (2018). Algorithmic regulation: A critical interrogation. Regulation & Governance, 12(4), 505–523. https://doi.org/10.1111/rego.12158
Zuboff, S. (2019). The age of surveillance capitalism: The fight for a human future at the new frontier of power. PublicAffairs.
