Conference Agenda

1.Problem Statement and Purpose

Platform economies have profoundly reshaped socio-economic power structures. The integration of Generative Artificial Intelligence (GenAI) is now driving a deeper, more fundamental structural transformation of this platform power. While platforms inherently seek to expand their influence, GenAI endows them with novel characteristics distinct from the traditional platform era. This transformation manifests in a shift of the power base from data monopoly to knowledge production and intelligent creation, and a heightened reliance on subtle conditioned power and deep preference shaping in operational mechanisms. Consequently, this leads to the cross-domain expansion of power, deeper real-world intervention, and vertical integration of power structures, ultimately exacerbating multiple socio-economic contradictions. Existing frameworks struggle to address the complexities arising from GenAI's unique capabilities in reshaping power dynamics, leading to risks of increased market imbalance, sophisticated cognitive manipulation, imbalanced value distribution, and systemic risks.

The purpose of this paper is to systematically analyze the structural reshaping of platform power in the age of GenAI, characterized by "intelligent empowerment." It aims to identify the new features of this evolved power, delineate the associated risks and governance challenges, and subsequently propose an adaptive governance framework to scientifically grasp and address these complex dynamics.

2.Methodology

Employing a qualitative, theory-driven analytical approach, this paper integrates and synthesizes three key theoretical frameworks to deconstruct and analyze platform power: Bourdieu's theory of capital (examining the transformation of economic, cultural, social, and symbolic capital), Galbraith's tripartite classification of power (analyzing the shift in power implementation mechanisms, particularly the dominance of conditioned power), and Lukes' three dimensions of power (revealing the deepening and expansion of influence in decision-making, agenda-setting). This multi-dimensional theoretical lens is applied to analyze the specific mechanisms through which GenAI reshapes platform power structures.

3.Findings

GenAI fundamentally reshapes platform power. Firstly, its core foundation shifts from data monopoly to knowledge production and intelligent creation, elevating cultural capital. Secondly, operational mechanisms increasingly rely on subtle, covert conditioned power, enhancing predictive and shaping capabilities. Thirdly, its influence expands significantly, enabling deep preference shaping via direct cognitive intervention and broader agenda-setting across domains. These changes result in a more complex, deeply entrenched, and far-reaching form of platform power with profound societal implications.

4.Proposal

To address GenAI-driven platform power challenges, this paper proposes an adaptive governance framework. It rests on three pillars: dynamic balancing (agile adjustment), collaborative governance (multi-stakeholder participation), and technology-enabled governance (leveraging tech for regulation). The framework advocates integrating legal, ethical, and technical standards, while enhancing institutional capacities for risk response, stakeholder empowerment, and intelligent governance tools.

5. References:

Luitse, Dieuwertje. Platform power in AI: The evolution of cloud infrastructures in the political economy of artificial intelligence. Internet Policy Review. Retrieved from https://www.econstor.eu/bitstream/10419/300740/1/1897437501.pdf

Zuboff, S. (2019). The Age of Surveillance Capitalism: The Fight for a Human Future at the New Frontier of Power. PublicAffairs. Retrieved from https://www.taylorfrancis.com/chapters/edit/10.4324/9781003320609-27/age-surveillance-capitalism-shoshana-zuboff

Goktas P. Ethics, transparency, and explainability in generative ai decision-making systems: A comprehensive bibliometric study[J]. Journal of Decision Systems, 2024: 1-29.Retrieved from https://www.tandfonline.com/doi/abs/10.1080/12460125.2024.2410042

6. Keywords

Generative AI; Platform Power; Adaptive Governance

Public trust in governance faces unprecedented erosion due to systemic failures: corruption enabled by legal-institutional gaps, AI’s dual role as both efficiency tool and ethical risk, ineffective capacity development, and wavering commitment to values-based principles. This crisis manifests in declining policy compliance, social fragmentation, and weakened democratic resilience. The problem is multifaceted: authorities violate accountability without consequence, AI introduces opaque decision-making and bias, and capacity programmes often lack real-world ethical grounding. This paper aims to present these interconnected challenges and propose actionable pathways to rebuild trust. Specifically, it examines (a) how political-economic transformations impact trust, (b) structural flaws permitting unethical conduct, (c) AI’s benefits versus risks in public administration, (d) evidence on capacity-building efficacy, and (e) the centrality of ethics, transparency, and accountability. The objective is to advance a holistic framework for ethical governance in the digital age.

The research employs a mixed-methods approach:

• Comparative analysis of institutional frameworks

• Case studies illustrating corruption vulnerabilities

• Literature review

• Policy benchmarking evaluating accountability tools like open-data initiatives and consequence management models. Evidence is drawn from governmental reports, academic journals, and interagency assessments.

Key findings reveal:

• Trust erosion correlates with exclusionary governance and unpunished corruption, reducing tax compliance and crisis cooperation.

• AI’s drawbacks—opacity and bias—outweigh benefits when unregulated, risking rights violations.

• Capacity programmes fail without sanctions for misconduct.

• Values-based principles like transparency directly reduce public protests.

To address these issues, the following is proposed:

1. Close legal gaps via enforceable accountability.

2. Regulate AI using EU-style risk classification, mandating human oversight and bias audits.

3. Revamp capacity programmes with real-world ethics simulations and consequence protocols.

4. Embed values through transparency platforms and integrity pacts.

References

1. OECD. (2024). Government at a Glance 2023: Transparency and Trust in Public Institutions. Paris: OECD Publishing.

2. European Commission. (2024). The Artificial Intelligence Act: Harmonised Rules for Trustworthy AI. Brussels: EU Official Journal.

3. Public Protector South Africa. (2025). State of Ethical Governance Report. Pretoria: Government Printer.

Problem statement and purpose

In an age of rapid development of artificial intelligence, human society is experiencing an unprecedented technological advancement, and AI is getting increasingly indispensable to man. Hence the judgement will be possibly made that humans are more costly and less “useful” than AI, resulting in man’s losing job opportunities to the new technology.

governments are seesawing between opposite definitions of government responsibilities when dealing with the relationship between AI advances and man’s development, when the conflicts between the two appear very sharp and profound. Then, how to reassure people of their value and at the same time help high-tech enterprises continue the development and application of artificial intelligence? How should government grasp the chance of the fourth industrial revolution without expanding the expense of society? If government does nothing when facing unemployment problems, social unfairness can not be automatically resolved, and humans’ living space will be continually narrowed, with the poor harder to support themselves. This paper tries to find, through enhancing government ethics of responsibility , reasonable solutions to the unbalanced development between man and AI.

Methodology

This paper will use the method of literature review, firstly, to find out what government ethics of responsibility is; secondly, to comb what government did when new technology was tending to take the place of human beings and thus formed a threat to human’s well being; thirdly, to show what government is doing to ease the pressure of AI development and application. This paper will employ the method of observation and comparison when displaying governments’ dealing with technological unemployment and analyzing ethics of responsibility within their decision and behavior.

Findings

This paper finds that in every time of technological innovation, when people are forced to compete against new inventions, they are likely to blame their unemployment to technological advances, with Luddite riots as a typical example. On the other side, different political parties in power have a different understanding of government roles: some would like to promote technology for economic growth, and others would prefer curbing technological development to solve the imbalance between technological advances and unemployment. Many governments are found to be oscillating between libertarianism and liberalism when dealing with technological progress and the unemployment caused by it.

Proposal

The paper proposes that government ethics of responsibility can’t be separated from that of conviction. It insists that governments should take seriously dialectics and theories of harmoniousness from Chinese traditional culture, such as the combination of Taoism, Confucianism and Legalism, and learn to promote both technological advances and human development by motivating people to actively embrace changes, and that governments should aim at the good end, make good policies to achieve good results. The paper believes that governments can harmonize the relationship between AI and man by enhancing man’s subjectivity of learning, working and social responsibility.

Reference

Coeckelbergh, Mark(2020). AI ethics. MIT Press.

Schwartz, A. (1982). Meaningful work. Ethics, 92(4), 634–646.

Kuzior, A(2022). Technological Unemployment in the Perspective of Industry 4.0. Virtual Economics, 1(5), 7-23.

1. Problem Statement and Objective

In the digital-intelligence era, traditional governance paradigms are beset by multifaceted challenges. Traditional governance, anchored in empirical judgment, suffers from insufficient decision-making precision. By stark contrast, while digital-intelligence technologies and artificial intelligence advance at a breakneck pace, organizational and institutional reforms lag significantly—hindered by path dependencies, cultural habits, and other systemic hurdles. This study zeroes in on the profound transformation of governance paradigms against the backdrop of cultural-technological integration in the digital-intelligence age, with its core focus on value reconstruction and capability iteration. It aims to unravel the mechanism through which innovative thinking—such as collaborative governance, catalyzed by cultural-technological integration—influences governance practices, thereby fueling the comprehensive elevation of governance efficacy.

2. Methodology

This research employs a systematic literature review to curate scholarly materials across digital-intelligence technologies, cultural-technological integration, and social governance. It dissects the application mechanisms of digital-intelligence technologies—including the Internet of Things (IoT), blockchain, and algorithmic models—in governance frameworks. Concurrently, it utilizes a case-based analytical approach, selecting paradigmatic cases of digital-intelligent governance to triangulate findings.

3. Findings

Digital-intelligence technologies, by virtue of panoramic data collection, deep mining, and intelligent analysis, transform fragmented social facts into structured knowledge graphs—thereby revolutionizing governance models from experience-intuition dominance to data-evidence primacy. The synergistic orchestration of IoT (Internet of Things), blockchain, and algorithmic models constructs a four-in-one governance ecosystem of "systematic, scientific, intelligent, and law-based" principles, propelling governance paradigms toward openness, dynamics, and comprehensiveness.

4. Proposal

Cultural-technological integration has not only furnished governance with novel tools but also spawned innovative paradigms—including collaborative governance, data-driven governance, and agile governance. Governance actors must construct a competency architecture attuned to the digital-intelligence era: through intelligent parsing of data elements and knowledge sharing, they should fortify decision-making and implementation capacities, thereby propelling the governance paradigm from a linear model to a three-dimensional, interactive, and collaborative ecosystem. This transformation ultimately aims to achieve the comprehensive elevation of governance efficacy and the modernization of social governance.

5. References:

[1] Guo, K. M. (2025). China's Industrial Structure Transformation from the Perspective of Production Network Evolution. Chinese Social Sciences Journal, (04), 97-118+206.

[2] Yu, N. P. (2025). The Reshaping of International Power in the Era of General Artificial Intelligence. Chinese Social Sciences Journal, (04), 41-59+205.

[3] Mi, J. N. (2024). Generative Governance: A New Paradigm of Governance in the Era of Large Language Models. Chinese Social Sciences Journal, (10), 119-139+207.