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Me or the Formula: What a Scored Person Can Learn in ASI 2030's Two Futures · 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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  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, platforms, 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.

CaseStudioRecord
A thirty percent cutASI 2030https://asi-2030.risconlabs.com/auditor?record=I1-03
Less work or a new formulaASI 2030https://asi-2030.risconlabs.com/auditor?record=I2-04
A parole risk scoreASI 2030https://asi-2030.risconlabs.com/auditor?record=I3-02
Days absentASI 2030https://asi-2030.risconlabs.com/auditor?record=I4-02
The union's accessASI 2030https://asi-2030.risconlabs.com/auditor?record=I5-05
The anonymous reviewerASI 2030https://asi-2030.risconlabs.com/auditor?record=I6-05
The repeat filersASI 2030https://asi-2030.risconlabs.com/auditor?record=I7-01
One question and twelve neighborsASI 2030https://asi-2030.risconlabs.com/auditor?record=I7-03
Two formulas and eight deliverablesASI 2030https://asi-2030.risconlabs.com/auditor?record=I8-04
The dispatch dropASI 2030https://asi-2030.risconlabs.com/auditor?record=II1-05
A mediation thresholdASI 2030https://asi-2030.risconlabs.com/auditor?record=II2-04
The drop before a loan renewalASI 2030https://asi-2030.risconlabs.com/auditor?record=II3-01
The internship assignmentASI 2030https://asi-2030.risconlabs.com/auditor?record=II3-05
Fewer appealsASI 2030https://asi-2030.risconlabs.com/auditor?record=II4-03
The training center's scoreASI 2030https://asi-2030.risconlabs.com/auditor?record=II6-04
The lower-ranked patientASI 2030https://asi-2030.risconlabs.com/auditor?record=II7-02
The unalignable flagASI 2030https://asi-2030.risconlabs.com/auditor?record=II7-03
The researcher's sampleASI 2030https://asi-2030.risconlabs.com/auditor?record=II8-05

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

Bibliography

  • Berk Ustun, Alexander Spangher and Yang Liu, Actionable Recourse in Linear Classification (2019).
  • Suresh Venkatasubramanian and Mark Alfano, The philosophical basis of algorithmic recourse (2020).
  • Amir-Hossein Karimi, Bernhard Schölkopf and Isabel Valera, Algorithmic Recourse: from Counterfactual Explanations to Interventions (2021).
  • Solon Barocas, Andrew D. Selbst and Manish Raghavan, The hidden assumptions behind counterfactual explanations and principal reasons (2020).
  • Reuben Binns, Max Van Kleek, Michael Veale, Ulrik Lyngs, Jun Zhao and Nigel Shadbolt, 'It's Reducing a Human Being to a Percentage': Perceptions of Justice in Algorithmic Decisions (2018).
  • Min Kyung Lee, Daniel Kusbit, Evan Metsky and Laura Dabbish, Working with Machines: The Impact of Algorithmic and Data-Driven Management on Human Workers (2015).
  • Alex J. Wood, Mark Graham, Vili Lehdonvirta and Isis Hjorth, Good Gig, Bad Gig: Autonomy and Algorithmic Control in the Global Gig Economy (2018).
  • Ryan Calo and Alex Rosenblat, The Taking Economy: Uber, Information, and Power (2017).
  • Ali Alkhatib and Michael Bernstein, Street-Level Algorithms: A Theory at the Margins of Practice (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