Enterprise AI Analysis
A Conceptual Crosswalk Between Trustworthy Records and Trustworthy AI
The rise of AI has intensified challenges of misinformation, opacity, and accountability. This paper critically examines how trustworthiness is conceptualized in archival science versus emerging frameworks of Trustworthy AI (TAI). By bridging these two fields, we uncover pathways to robust, evidence-based AI governance, transforming trustworthiness from a technical checklist into an ongoing institutional commitment.
Authors: Laís Barbudo Carrasco, Tove Sofia Engvall, Felix Dobslaw
Executive Impact Summary
This analysis provides a strategic overview for leaders navigating AI implementation, emphasizing the critical intersection of data governance, accountability, and long-term trust.
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Papers Highlighting Transparency
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Papers Highlighting Explainability
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Papers Highlighting Fairness
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Papers Lacking Explicit Definitions for TAI
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Key Concepts
Crosswalk Details
Scoping Review Findings
Implementation Insights
Understanding Trust in AI and Records
Trust is foundational for society, yet AI's rapid ascent introduces significant challenges related to misinformation, opacity, and accountability. This study examines trustworthiness through two lenses: archival science, which grounds it in records' enduring qualities (authenticity, reliability, integrity, usability), and Trustworthy AI (TAI) frameworks, which emphasize system-level attributes like transparency, explainability, fairness, and reliability.
Our research questions explore:
- RQ1: How is trustworthiness conceptualized in archival science compared to the emerging frameworks of TAI?
- RQ2: What are common characteristics when defining Trust and Trustworthiness in AI?
- RQ3: In what ways can the archival attributes of authenticity, reliability, integrity, and usability inform and extend the principles of TAI?
Archival Trust Attributes vs. Trustworthy AI Principles
Our conceptual crosswalk between ISO 15489-1:2016 (archival science) and NIST AI 100-3:2024 (Trustworthy AI) reveals both shared vocabulary and divergent emphases, providing a rich basis for integrating robust governance principles.
| Archival Attribute | Archival Definition/Emphasis | TAI Principle/Emphasis |
|---|---|---|
| Authenticity |
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| Reliability |
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| Integrity |
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| Usability |
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Trustworthy AI Characteristics & Definitional Clarity
Our scoping review of 151 papers on Trustworthy AI highlights a strong focus on certain characteristics, while revealing a significant gap in explicit definitional consensus.
29.9%
of Trustworthy AI papers offer an explicit definition of trustworthiness.
This suggests that while the field recognizes the importance of conceptual clarity, most papers operationalize trustworthiness through characteristics and practices rather than formal definitions. This creates challenges for establishing shared foundational standards in TAI governance.
Enterprise Process Flow: Research Methodology
Conceptual Crosswalk (ISO 15489-1:2016 & NIST AI 100-3:2024)
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Scoping Literature Review (151 TAI Papers)
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LLM-assisted Content Analysis
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Human Validation & Term Consolidation
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Identify Conceptual Parallels & Divergences
Integrating Archival Perspectives for Robust TAI Governance
Archival science offers a robust, evidence-based framework that can deepen TAI's conceptual foundations, moving beyond technical checklists to embedded institutional practices.
Case Study: Enhancing AI Accountability with Archival Provenance
A financial institution deploys an AI system for loan approvals. Initially, the system's decisions lack clear audit trails, making it difficult to explain denied applications. By integrating archival principles, the institution implemented a provenance framework:
- Documentation: Every input (training data versions, models, parameters, developer notes) and output (decision, confidence score) is captured as a "record."
- Metadata & Context: Rich metadata accompanies each record, including timestamps, responsible agents (human and AI), and decision rules applied.
- Integrity & Authenticity: Blockchain-based hashing ensures the immutability of these records, verifying their authenticity over time.
- Lifecycle Accountability: Automated retention policies and periodic audits ensure that decision records are preserved and accessible for regulatory compliance and dispute resolution.
Result: The institution improved regulatory compliance, significantly reduced legal risks, and enhanced public trust by providing transparent, verifiable explanations for AI-driven decisions. This demonstrates how archival principles transform AI trustworthiness into an ongoing, auditable governance function.
Quantify Your AI Trustworthiness ROI
Estimate the potential gains from implementing robust AI trustworthiness and data governance frameworks in your enterprise.
Your Trustworthy AI Implementation Roadmap
A phased approach integrating archival rigor with TAI best practices to build sustainable trustworthiness in your AI systems.
Phase 1: Assess Current AI Systems & Data Governance
Conduct an audit of existing AI deployments, data management practices, and record-keeping systems. Identify critical touchpoints where AI generates or impacts evidentiary records and assess current transparency, accountability, and ethical safeguards. Establish a baseline for trustworthiness.
Phase 2: Define Trustworthiness Requirements (Archival & TAI)
Integrate archival attributes (authenticity, reliability, integrity, usability) with TAI principles (transparency, explainability, fairness, robustness, privacy) to develop comprehensive, context-specific trustworthiness requirements for AI systems and their outputs. Prioritize requirements based on risk and societal impact.
Phase 3: Develop Documentation & Provenance Frameworks
Design and implement robust systems for capturing the provenance of AI-generated information, including training data, model versions, algorithmic decisions, and human interventions. Establish metadata standards to ensure context, authenticity, and interpretability of AI outputs as records over time.
Phase 4: Implement Lifecycle Accountability & Audit Trails
Establish mechanisms for continuous monitoring, auditing, and verification of AI system performance and record integrity. Develop policies for long-term preservation of AI-generated records, ensuring their accessibility and evidential value for future accountability, legal, and historical purposes.
Phase 5: Continuous Monitoring & Ethical Oversight
Implement ongoing processes for ethical review, stakeholder engagement, and adaptive governance to address emerging risks and ensure AI systems remain aligned with societal values and regulatory standards. Foster a culture of trustworthiness across the organization.
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