The Corrigibility Window: Correcting AI-Assisted Decisions in Time

The Corrigibility Window is a six-month independent research project developing a temporal framework for evaluating the practical corrigibility of AI-assisted institutional decisions.

The project asks a simple but underexamined question: when an error or problematic decision is detected, is there still enough time for review and intervention to produce an effective correction?

Existing oversight frameworks often focus on whether humans retain formal authority, appeal mechanisms, transparency, or the technical ability to override a system. These properties matter, but they do not by themselves establish that correction remains practically reachable.

The project develops the distinction between formal reversibility and corrective reachability. It examines how detection latency, escalation, review, institutional response, and the timing of intervention interact with the point at which consequences become effectively irreversible.

The work will produce documented case studies, a taxonomy of corrigibility failures, an evaluation matrix for temporal corrigibility, external methodological review, an open assessment framework, and a roadmap for subsequent empirical testing.

The project is led by Andrés Palladino, an independent researcher based in Tromsø, Norway. It builds on two related theoretical research papers currently under peer review and extends that conceptual work toward an operational framework for AI-assisted decision systems.

El texto ya tiene las palabras que quiero que vea un financiador: oversight, intervention, corrective reachability, operational framework, case studies, empirical testing.