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Reasons Without the Reasoning: What an Auditor Needs to See in ASI 2030's Judgment Transparency 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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Reasons Without the Reasoning: What an Auditor Needs to See in ASI 2030's Judgment Transparency 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

  • Zachary C. Lipton, The Mythos of Model Interpretability (2018).
  • Tim Miller, Explanation in artificial intelligence: Insights from the social sciences (2019).
  • Brent Mittelstadt, Chris Russell and Sandra Wachter, Explaining Explanations in AI (2019).
  • Cynthia Rudin, Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead (2019).
  • Marco Tulio Ribeiro, Sameer Singh and Carlos Guestrin, "Why Should I Trust You?": Explaining the Predictions of Any Classifier (2016).
  • Andrew D. Selbst and Solon Barocas, The Intuitive Appeal of Explainable Machines (2018).
  • Sandra Wachter, Brent Mittelstadt and Luciano Floridi, Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation (2017).
  • Lilian Edwards and Michael Veale, Slave to the Algorithm? Why a 'right to an explanation' is probably not the remedy you are looking for (2017).
  • Margot E. Kaminski, The Right to Explanation, Explained (2018).
  • Reuben Binns, Algorithmic Accountability and Public Reason (2017).

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