Enterprise AI Analysis
A Call for Transdisciplinary Trust Research in the Artificial Intelligence Era
Authors: Frank Krueger, René Riedl, Jennifer A. Bartz, Karen S. Cook, David Gefen, Peter A. Hancock, Sirkka L. Jarvenpaa, Lydia Krabbendam, Mary R. Lee, Roger C. Mayer, Alexandra Mislin, Gernot R. Müller-Putz, Thomas Simpson, Haruto Takagishi & Paul A. M. Van Lange
The rapid integration of AI into daily life presents grand societal challenges that necessitate a fundamental re-evaluation of trust. Traditional interpersonal trust concepts are insufficient for human-AI interaction. This analysis advocates for a transdisciplinary approach, integrating diverse scientific and stakeholder perspectives, to build and bolster trust in AI amidst risks like misinformation, discrimination, and autonomous warfare.
Executive Impact & Key Metrics
Our analysis highlights the profound societal and economic implications of AI, underscoring the urgent need for a transdisciplinary approach to trust.
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AI's Projected Boost to Global Economy by 2030
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Trust Research Articles Reviewed
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Studies Lacking Stakeholder Input in Trust Research
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Grand Societal Challenges Addressed by AI (e.g., Misinformation, Warfare)
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Core Challenges
Transdisciplinary Framework
Real-World Application
Understanding AI's Societal Challenges and Trust Dimensions
Artificial Intelligence presents complex societal challenges that directly impact trust. Each challenge intertwines with specific trustworthiness elements, risks, user groups, interaction spheres, and operational terrains, as illustrated below. Addressing these requires a nuanced, multi-faceted approach.
| Grand Challenge | Trustworthiness | Risk | User | Sphere | Terrain |
|---|---|---|---|---|---|
| Profiling | Privacy | Machine Learning: Prediction | Consumer | Self | Advertising |
| Misinformation | Non-maleficence | Computer Vision: Deepfake | Social Media User | Stranger | Social Media |
| Discrimination | Fairness | Natural Language Processing: Bias | Job Applicant | Race Minority | Job Recruitment |
| Job Displacement | Accountability | AI-powered Robotics: Autonomy | Retail Staff | Cooperation | Retail |
| Warfare | Explainability | Deep Learning: Opacity | Military Personnel | Nation | Military |
| Singularity | Human-Centricity | Quantum-Enhanced AI: Supremacy | Humanity | AI Evolution | Governance |
The Transdisciplinary Research Framework (T-R-U-S-T)
To effectively address AI trust challenges, the research proposes a transdisciplinary framework that bridges academic disciplines and integrates stakeholder perspectives across three key phases, centered on five elements of trust: trustworthiness, risk, user, sphere, and terrain.
Enterprise Process Flow
Phase I: Problem Transformation
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Phase II: Production of New, Connectable Knowledge
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Phase III: Transdisciplinary Integration
Phase I: Problem Transformation involves identifying societal grand challenges, linking them to existing scientific knowledge gaps, and redefining them as a common research objective. This stage sets the foundation for a unified approach.
Phase II: Production of New, Connectable Knowledge focuses on delineating the roles of scientists and stakeholders and developing an integration concept. This concept is built around five core elements of trust: trustworthiness, risk, user, sphere, and terrain, ensuring a holistic understanding.
Phase III: Transdisciplinary Integration assesses the integrated results and compiles actionable outputs for both societal and scientific communities. This continuous feedback loop fosters iterative improvement and ensures solutions are relevant and impactful, bridging the real-world pathway with intra-scientific discovery.
Case Study: Building Trust in Autonomous Vehicles (AVs)
The framework's practical utility can be seen in addressing trust failures, such as incidents involving autonomous vehicles, which highlight the complexity of human-AI trust dynamics in safety-critical domains.
Autonomous Vehicles: Addressing Trust Failures
Context: Recent incidents with autonomous vehicles (AVs), like the Cruise robotaxi in San Francisco, led to revoked operating licenses and significant public distrust. Despite AVs being statistically safer than human-driven cars, concerns persist regarding opaque decision-making and ethical dilemmas in complex scenarios.
The Challenge: To address compromised safety, reliability, explainability, and accountability, which erode public trust in autonomous driving.
Transdisciplinary Approach:
- Stakeholder Engagement: Structured interviews and focus groups with diverse groups including vehicle manufacturers, tech companies, regulators, public commuters, technologists, and legal experts.
- Interdisciplinary Scientific Collaboration: Bringing together experts from automotive engineering, transportation science, computer science, ergonomics, information systems, psychology, philosophy, ethics, and law.
- Holistic Strategy: Devise all-encompassing trust-building solutions for AVs by integrating technological, psychological, ethical, legal, and socio-economic aspects.
- Integration Concept Focus: Develop clear AI decision-making models for risky situations (trustworthiness, risk), address specific transportation needs across urban/rural communities (sphere, terrain), and ensure respect for cultural norms and ethical standards.
Anticipated Outcomes: Development of a comprehensive guide for key decision-makers, scientific findings shared in peer-reviewed journals, and influencing societal & regulatory discussions. This fosters a lasting impact on AVs and trust.
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Our Transdisciplinary Implementation Roadmap
Implementing a transdisciplinary trust framework requires a structured approach across distinct phases, ensuring collaboration and practical impact.
Phase 1: Problem Transformation
This initial phase focuses on clearly defining the grand societal challenge related to AI trust. It involves: Inception of the project (identifying real-world incidents like AV failures), Crafting the grand challenge (through stakeholder interviews and socio-empirical methodologies), Connecting the challenge to scientific knowledge (linking to existing knowledge gaps across disciplines), and Transforming into a common research object (forming a transdisciplinary team to frame questions and hypotheses).
Phase 2: Production of New Connectable Knowledge
In this phase, a collaborative environment is established to generate insights and solutions. Key steps include: Clarification of roles for scientists and stakeholders (data generation/analysis vs. practical insights/resources), Design of an integration concept (incorporating trustworthiness, risk, user, sphere, and terrain for unified knowledge), and Implementation of the integration concept (developing and testing strategies with stakeholder feedback).
Phase 3: Transdisciplinary Integration
The final phase focuses on evaluating and disseminating the results for both societal and scientific impact. This involves: Assessing the integrated results (collaborative evaluation of trust-enhancement strategies with stakeholder and scientific input), Compiling outputs for society and science (developing comprehensive guides, peer-reviewed articles, and books), and understanding the Consequences of the project on societal and scientific discourses (influencing local institutions, regulatory bodies, and promoting further research).
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