ENTERPRISE AI ANALYSIS
A Network Approach to Public Trust in Generative AI
This analysis re-evaluates public trust in generative AI, moving beyond industry-centric frameworks to a network-based approach. It highlights that trust in AI is intrinsically linked to the broader information environment and the trustworthiness of diverse social actors, challenging current policy perspectives.
Executive Impact: Redefining AI Trust for Enterprise
For enterprise leaders, understanding public trust in AI is no longer a confined technical or legal challenge. Our research reveals that generative AI acts as a "social actor" within a complex network, significantly influencing information environments. This necessitates a "whole-of-society" approach to AI policy, extending beyond industry regulation to address the broader ecosystem of information and social trust.
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Current approaches for Trustworthy AI are limited in scope
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Generative AI systems function as active social actors
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Broader network of social actors influencing AI trust
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Philosophy of AI
Socio-Technical Systems
Trust & Ethics
Generative AI as Poietic Agents
Generative AI technologies challenge traditional views of technology as mere tools. They are instead posited as "poietic agents", capable of processing information and actively participating in the production of semantic artifacts. This includes creating original human-like communications, shaping socio-political narratives, and influencing collective knowledge. This module highlights the profound shift from AI as a passive mediator to an active social participant with inherent (though non-intentional) agency.
Synthetic Socio-technical Systems
The deep social integration of generative AI transforms traditional socio-technical systems into "synthetic" ones. Unlike previous technologies, generative AI comes to the foreground of sociality, operating as unpredictable social actors within our broader social practices. Trust, in this context, emerges from the complex and precarious material conditions and interactions within these vast, interconnected networks, which extend far beyond the immediate AI industry.
Network Approach to Trust
This paper argues that the traditional concept of interpersonal trust cannot be directly applied to AI. Instead, a network approach to trust is proposed, where trustworthiness is a product of the material conditions and social interactions among a diverse network of human and non-human actors. This holistic view acknowledges that public trust in AI is influenced by the integrity of the broader information environment and the trustworthiness of institutions and political discourse.
Trustworthy AI Framework: Limitations vs. Network Approach |
Traditional Trustworthy AI | Network Approach |
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"Social Actors"
Generative AI systems are not mere tools but active participants in our social practices, shaping collective knowledge and discourse.
Emergence of Trust in the AI Network
Diverse Social Actors (Human & Non-human)
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Material Interactions & Entanglements
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Network Configuration (Ordered/Stabilized)
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Punctualization (Apparent Coherence)
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Trust in AI System/Outputs
The Post-Truth Crisis and AI Trust (Brexit Example)
The trustworthiness of generative AI is deeply entwined with the current post-truth political crisis. As demonstrated by the Brexit discourse, widespread public distrust in traditional institutions (such as political figures, media organizations, and academic experts) and the prevalence of online disinformation significantly undermine the perceived reliability of information. When AI outputs are synthesized from these very same sources, their trustworthiness is directly compromised.
Enterprise Implication: For organizations deploying AI, ensuring public trust requires not only robust AI systems but also a proactive strategy to navigate and counteract the broader information environment's challenges, including combating disinformation and fostering trust in reliable information sources.
Calculate Your Potential AI Impact
Estimate the potential efficiency gains and cost savings for your organization by integrating a network-aware AI strategy.
Annual Cost Savings
Hours Reclaimed Annually
Your AI Trust & Implementation Roadmap
A successful enterprise AI strategy focused on public trust requires a structured approach, integrating social and technical considerations from the outset.
Phase 1: Network Mapping & Risk Assessment
Identify all relevant social actors (internal, external, information sources) influencing trust in your AI applications. Assess potential vulnerabilities in the information environment.
Phase 2: Policy Integration & Ethical Governance
Develop policies that transcend internal AI regulation to address broader information integrity. Establish ethical guidelines that consider AI's role as a social actor.
Phase 3: Stakeholder Engagement & Transparency
Actively engage with diverse stakeholders, including the public, to build confidence. Implement transparency measures for AI outputs and underlying data sources, even those external to your direct control.
Phase 4: Continuous Monitoring & Adaptability
Establish systems to continuously monitor AI's impact on public discourse and trust. Be prepared to adapt policies and AI deployments in response to evolving social dynamics and information environments.
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