Paper Citation Record · LEDGER

Accountability of Robust and Reliable AI-Enabled Systems: A Preliminary Study and Roadmap

As of 21 August 2026, this Paper Citation Record lists 28 of 28 outbound references and 0 inbound Pith citation observations for arXiv:2506.16831.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2506.16831 v1

Coverage vector

measured 28 of 28 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T23:39:54.314250Z

measured 28 of 28 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

28 of 28 outbound references displayed

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External citation measurements

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Outbound references

Observation ca9969e9-ea86-4303-8f3b-d5d5550f2f64 · outbound

This paper cites Trustworthy AI: From Principles to Practices.ACM Computing Surveys, 55(9):1–46, September 2023.

Accountability of Robust and Reliable AI-Enabled Systems: A Preliminary Study and Roadmap Trustworthy AI: From Principles to Practices.ACM Computing Surveys, 55(9):1–46, September 2023

Reference 1

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This paper cites Bogen and A.

Accountability of Robust and Reliable AI-Enabled Systems: A Preliminary Study and Roadmap Bogen and A

Reference 2

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This paper cites Boudette.

Accountability of Robust and Reliable AI-Enabled Systems: A Preliminary Study and Roadmap Boudette

Reference 3

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This paper cites https://www.carnegiecouncil.org/explore-engage/key-terms/ai- governance.

Accountability of Robust and Reliable AI-Enabled Systems: A Preliminary Study and Roadmap https://www.carnegiecouncil.org/explore-engage/key-terms/ai- governance

Reference 4

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Accountability of Robust and Reliable AI-Enabled Systems: A Preliminary Study and Roadmap Unresolved cited work

Reference 5

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This paper cites Oxford University Press, August 1997.

Accountability of Robust and Reliable AI-Enabled Systems: A Preliminary Study and Roadmap Oxford University Press, August 1997

Reference 6

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This paper cites Microsoft Press, Redmond, Washington, second edition edition, 2004.

Accountability of Robust and Reliable AI-Enabled Systems: A Preliminary Study and Roadmap Microsoft Press, Redmond, Washington, second edition edition, 2004

Reference 7

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This paper cites https://bertrandmeyer.com/oosc2/.

Accountability of Robust and Reliable AI-Enabled Systems: A Preliminary Study and Roadmap https://bertrandmeyer.com/oosc2/

Reference 8

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Accountability of Robust and Reliable AI-Enabled Systems: A Preliminary Study and Roadmap Unresolved cited work

Reference 9

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Accountability of Robust and Reliable AI-Enabled Systems: A Preliminary Study and Roadmap Unresolved cited work

Reference 10

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Observation 98d34292-d66d-4609-9bb9-a4e923b6bdea · outbound

This paper cites Towards Account- ability for Machine Learning Datasets: Practices from Software Engineering and Infrastructure.

Accountability of Robust and Reliable AI-Enabled Systems: A Preliminary Study and Roadmap Towards Account- ability for Machine Learning Datasets: Practices from Software Engineering and Infrastructure

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:39:54.566233Z

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Observation 80913107-fc48-4b27-8c1e-af36a25eccbb · outbound

This paper cites Reliability, Robustness, and Resilience: The R3 Concept in Machine Learning Inference, July 2023.

Accountability of Robust and Reliable AI-Enabled Systems: A Preliminary Study and Roadmap Reliability, Robustness, and Resilience: The R3 Concept in Machine Learning Inference, July 2023

Reference 12

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Observation a383f745-4dd4-433f-a51a-0a8874cc2bf2 · outbound

This paper cites https://encord.com/blog/model- robustness-machine-learning-strategies/.

Accountability of Robust and Reliable AI-Enabled Systems: A Preliminary Study and Roadmap https://encord.com/blog/model- robustness-machine-learning-strategies/

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:39:54.546309Z

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This paper cites Freeman, and Xinwei Deng.

Accountability of Robust and Reliable AI-Enabled Systems: A Preliminary Study and Roadmap Freeman, and Xinwei Deng

Reference 14

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Observation cf85f060-2cde-41fe-8c57-203345119f85 · outbound

This paper cites Blood, Nathan W.

Accountability of Robust and Reliable AI-Enabled Systems: A Preliminary Study and Roadmap Blood, Nathan W

Reference 15

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Observation 9edf3d07-ecfb-4ff3-a85b-c214d246d644 · outbound

This paper cites Trustworthy and Robust AI Deployment by Design: A framework to inject best practice support into AI deployment pipelines.

Accountability of Robust and Reliable AI-Enabled Systems: A Preliminary Study and Roadmap Trustworthy and Robust AI Deployment by Design: A framework to inject best practice support into AI deployment pipelines

Reference 16

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Observation c2ed98a6-3c29-42ec-a13b-3b2a393e3e50 · outbound

This paper cites AI Maintenance: A Robustness Perspective.Com- puter, 56(2):48–56, February 2023.

Accountability of Robust and Reliable AI-Enabled Systems: A Preliminary Study and Roadmap AI Maintenance: A Robustness Perspective.Com- puter, 56(2):48–56, February 2023

Reference 17

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Accountability of Robust and Reliable AI-Enabled Systems: A Preliminary Study and Roadmap Unresolved cited work

Reference 18

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This paper cites White, Margaret Mitchell, Timnit Gebru, Ben Hutchinson, Jamila Smith-Loud, Daniel Theron, and Parker Barnes.

Accountability of Robust and Reliable AI-Enabled Systems: A Preliminary Study and Roadmap White, Margaret Mitchell, Timnit Gebru, Ben Hutchinson, Jamila Smith-Loud, Daniel Theron, and Parker Barnes

Reference 19

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Observation 92f9f4b7-b752-4793-98ba-f8398229fab2 · outbound

This paper cites Machine Learning Op- erations(MLOps):Overview,Definition,andArchitecture.

Accountability of Robust and Reliable AI-Enabled Systems: A Preliminary Study and Roadmap Machine Learning Op- erations(MLOps):Overview,Definition,andArchitecture

Reference 20

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Observation e3eb0309-ac18-4bc4-9b41-8e0d7a759429 · outbound

This paper cites AI governance in the system development life cycle: Insights on respon- sible machine learning engineering.

Accountability of Robust and Reliable AI-Enabled Systems: A Preliminary Study and Roadmap AI governance in the system development life cycle: Insights on respon- sible machine learning engineering

Reference 21

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This paper cites Assuring the Machine Learning Lifecycle: Desiderata, Methods, and Challenges.

Accountability of Robust and Reliable AI-Enabled Systems: A Preliminary Study and Roadmap Assuring the Machine Learning Lifecycle: Desiderata, Methods, and Challenges

Reference 22

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Observation af09e1d3-ef62-4862-9871-570082cbc414 · outbound

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Accountability of Robust and Reliable AI-Enabled Systems: A Preliminary Study and Roadmap Unresolved cited work

Reference 23

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This paper cites MLOps as Enabler of Trustworthy AI.

Accountability of Robust and Reliable AI-Enabled Systems: A Preliminary Study and Roadmap MLOps as Enabler of Trustworthy AI

Reference 24

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This paper cites QoA4ML - A Framework for Supporting Contracts in Machine Learning Services.

Accountability of Robust and Reliable AI-Enabled Systems: A Preliminary Study and Roadmap QoA4ML - A Framework for Supporting Contracts in Machine Learning Services

Reference 25

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This paper cites Design by Contract for Deep Learning APIs.

Accountability of Robust and Reliable AI-Enabled Systems: A Preliminary Study and Roadmap Design by Contract for Deep Learning APIs

Reference 26

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Observation b117067f-e047-45c1-9062-f7eb55c43820 · outbound

This paper cites Building Your Own Product Copilot: Challenges, Opportunities, and Needs.

Accountability of Robust and Reliable AI-Enabled Systems: A Preliminary Study and Roadmap Building Your Own Product Copilot: Challenges, Opportunities, and Needs

Reference 27

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Accountability of Robust and Reliable AI-Enabled Systems: A Preliminary Study and Roadmap Unresolved cited work

Reference 2021

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Pith citing papers

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