> ## Content Index
> Fetch the complete content index at: https://www.aigl.blog/llms.txt
> Use this file to discover other available public pages before exploring further.

# A Taxonomy of Trustworthiness for Artificial Intelligence
- URL: https://www.aigl.blog/a-taxonomy-of-trustworthiness-for-artificial-intelligence/
- Published: 2025-04-07T18:24:55.000Z
- Updated: 2026-09-12T20:05:25.000Z
- Description: That’s the question this report tackles—offering one of the most detailed attempts so far to break down “trustworthy AI” into 150 actionable properties across the AI lifecycle. The taxonomy isn’t just a list.
- Author: Jakub Szarmach
- Tags: Glossary, UC Berkeley, Guide, #aigl-library

[Taxonomy\_of\_AI\_TrustworthinessTaxonomy\_of\_AI\_Trustworthiness.pdf5 MBdownload-circle](https://www.aigl.blog/content/files/2025/04/Taxonomy%5Fof%5FAI%5FTrustworthiness.pdf "Download")

## **What’s Covered?**

The report builds directly on the NIST AI Risk Management Framework (AI RMF) and expands its categories—validity, safety, security, accountability, explainability, privacy, fairness—into a working inventory of questions and practices. To this foundation, the author adds an eighth category: **Responsible Practice and Use**, emphasizing AI’s social and organizational dimensions. Each property in the taxonomy is matched to a specific lifecycle stage, from business case design to monitoring, and is accompanied by guiding questions.

### **Document Highlights:**

- **Intro & Framing:** Identifies the fragmentation in how “trustworthy AI” is interpreted across sectors.
- **Comparative Analysis:** Summarizes existing frameworks like ALTAI, the AI Act, the AI Bill of Rights, and the NIST RMF.
- **Taxonomy Structure:** Maps 150 trustworthiness properties across seven AI lifecycle stages and eight trust dimensions.
- **Human-AI Engagement Spectrum:** Recognizes that not all systems are human-facing and adjusts the taxonomy accordingly.
- **Use Cases:** Designed to support developers, teams writing model/system cards, policymakers, and auditors.

## **💡 Why it matters?**

This paper stands out by translating high-level goals into specific, operational properties that can guide real development and assessment work. Instead of offering yet another set of values or guidelines, it asks: *What does it actually mean to be trustworthy, here, now, in this part of the process?* And it tries to answer that with targeted prompts and context-aware framing. The inclusion of “Responsible Practice and Use” is a valuable reminder that trust doesn’t only live in code or models—it’s about people and organizations too.

## **What’s Missing?**

This taxonomy is not meant to be a checklist—and the author is clear about that. But users unfamiliar with the NIST RMF or new to lifecycle-based governance might need additional scaffolding to make use of it. There’s also little discussion of how to prioritize among the 150 properties, or how resource-constrained teams might work with them. And although the taxonomy is designed to support diverse AI systems (not just human-facing ones), many properties still skew toward systems that affect individuals.

More practical guidance or toolkits based on this taxonomy would be a welcome next step.

## **Best For:**

- AI teams developing internal trust checklists or model documentation templates
- Standards-setting bodies and regulators aligning with the NIST RMF
- Auditors and independent reviewers needing a comprehensive question set
- Policy researchers mapping trust and safety dimensions across AI use cases

## **Source Details:**

**Jessica Newman** is Director of the AI Security Initiative at UC Berkeley’s Center for Long-Term Cybersecurity (CLTC), where she works at the intersection of cybersecurity, responsible AI, and governance. This white paper was informed by a 2022 expert workshop co-hosted with Intel and builds on dozens of interviews, policy sources, and prior toolkits. The document is part of CLTC’s white paper series and is available under open access.