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.
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).