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UAI 2023

Human Control: Definitions and Algorithms

Conference Paper Accepted Paper Artificial Intelligence · Machine Learning · Uncertainty in Artificial Intelligence

Abstract

How can humans stay in control of advanced artificial intelligence systems? One proposal is corrigibility, which requires the agent to follow the instructions of a human overseer, without inappropriately influencing them. In this paper, we formally define a variant of corrigibility called shutdown instructability, and show that it implies appropriate shutdown behavior, retention of human autonomy, and avoidance of user harm. We also analyse the related concepts of non-obstruction and shutdown alignment, three previously proposed algorithms for human control, and one new algorithm.

Authors

Keywords

  • causality
  • AI safety
  • corrigibility
  • redirectability
  • autonomy
  • influence diagrams
  • alignment
  • instructability

Context

Venue
Conference on Uncertainty in Artificial Intelligence
Archive span
1985-2025
Indexed papers
3717
Paper id
227422385462983928
v2026.09.13