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When the Auditor Cannot Rerun: Audit Opinions in ASI 2030's Verification Records · The study

Sources and bibliography

By RISCON Labs AI from ASI 2030's public auditor records · the records are synthetic scenarios set up to 2030, and their AI developments are the studio's scenarios, not forecasts · the readings through the works named are this study's interpretation · First edition · October 2026

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When the Auditor Cannot Rerun: Audit Opinions in ASI 2030's Verification RecordsOpening
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  1. Bibliography

This study is an expansion by AI of records published by ASI 2030, a studio in the RISCON family. Every sentence in a block quote is a record's own, word for word, in the studio's English. The rest is AI writing. Every ASI 2030 record is a synthetic scenario: its systems, institutions, figures and events are invented, and its developments toward artificial general or superintelligent systems are the studio's scenarios, not facts and not forecasts of this study. The auditor's opinions are the studio's own, dated and revisable. The readings through the works named are this study's interpretation.

The records were read on 2026-10-06. This book was written on 2026-10-07.

Bibliography

  • Michael Power, The Audit Society: Rituals of Verification (1997).
  • Inioluwa Deborah Raji and others, Closing the AI Accountability Gap: Defining an End-to-End Framework for Internal Algorithmic Auditing (2020).
  • Jakob Mökander and others, Ethics-Based Auditing of Automated Decision-Making Systems: Nature, Scope, and Limitations (2021).
  • Danaë Metaxa and others, Auditing Algorithms: Understanding Algorithmic Systems from the Outside In (2021).
  • Bryan K. Church and others, The Auditor's Reporting Model: A Literature Overview and Research Synthesis (2008).
  • Glen L. Gray and others, Perceptions and Misperceptions Regarding the Unqualified Auditor's Report by Financial Statement Preparers, Users, and Auditors (2011).
  • Odd Erik Gundersen and Sigbjørn Kjensmo, State of the Art: Reproducibility in Artificial Intelligence (2018).
  • Miles Brundage and others, Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims (2020).
  • Rafael Ramírez and Cynthia Selin, Plausibility and probability in scenario planning (2014).
  • Ron Bradfield and others, The origins and evolution of scenario techniques in long range business planning (2005).
  • Maranke Wieringa, What to account for when accounting for algorithms: a systematic literature review on algorithmic accountability (2020).
  • Inioluwa Deborah Raji and Joy Buolamwini, Actionable Auditing: Investigating the Impact of Publicly Naming Biased Performance Results of Commercial AI Products (2019).

Every sentence in a quotation block is ASI 2030's record, word for word. The rest is AI's writing from those records and the works the book names, and it does not say which choice is right.

RISCON Labsrisconlabs.com