Towards A Framework for Levels of Anthropomorphic Deception in Robots and AI
Franziska Babel, Shane Saunderson, Shalaleh Rismani
- Year
- 2026
- Access
- Open access
Abstract
This paper presents a preliminary draft of a framework around the use of anthropomorphic deception, defined here as misleading users towards humanlike affordances in the design of autonomous systems. The goal is to promote reflection among HCI and HRI researchers, as well as industry practitioners, to think about levels of anthropomorphic design that are: a) functionally necessary, b) socially appropriate, and c) ethically permissible for their use case. By reviewing the relevant literature on deception in HCI and HRI, we propose a framework with four levels of anthropomorphic deception. These levels are defined and distinguished by three factors: humanlikeness, agency, and selfhood. Example use cases at each level illustrate considerations around their functional, social, and ethical permissibility. We then present how this framework is applicable to previous work on persuasive robots We hope to promote a balanced view on anthropomorphic deception by design that should be neither naïve (e.g., as a default) nor exploitive (e.g., for economic benefit).
Keywords
Related papers
Statistical Learning Theory
Yuhai Wu, Vladimir Vapnik
1999
Fractional Differential Equations
Igor Podlubný
2025
Applied Nonlinear Control
Jean-Jacques Slotine, Weiping Li
1991
Genetic Programming: On the Programming of Computers by Means of Natural Selection
John R. Koza
1992