The ethics of bioinspired animal-robot interaction: A relational meta-ethical approach

In this article, I focus on a specific aspect of biorobotics: biohybrid interaction between bioinspired robots and animals. My goal is to analyze the ethical and epistemic implications of this practice, starting with a central question : Is it ethically permissible to have a bioinspired robot that mimics and reproduces the behaviors and/or morphology of an animal interact with a particular population, even if the animals do not know that the object they are interacting with is a robot and not a conspecific? My answer to the ethical question is that the interaction between animals and bioinspired robots is ethically acceptable if the animal actively participates in the language game (sense Coeckelbergh) established with the robot. I proceed as follows: First, I define the field of biorobotics and describe its four macro-categories. Second, I present concrete examples of interactive biorobotics, showing two emblematic cases in which the relationship between bioinspired robots and animals plays a central role. Third, I address one key issue—among many—in applied ethics regarding my ethical question. Fourth, I explore the ethical question on a metaethical level, making use of the theories of David Gunkel and Mark Coeckelbergh, as well as the linguistic approach and ethics of the late Ludwig Wittgenstein. Last, I argue that from a meta-ethical approach the original ethical question turns out to be misplaced. The ethical boundary lies not in the distinction between a real or fake relationship between the robot and the organism, but in the degree of mutual participation and understanding between the entities involved.

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Publication Details

Journal
Journal of Responsible Technology
Published
2025-03-23
DOI
https://doi.org/10.1016/j.jrt.2025.100116
Citations
3
Primary Topic
Robot Manipulation and Learning
Type
article
Field-Weighted Citation Impact
3.64
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article

The ethics of bioinspired animal-robot interaction: A relational meta-ethical approach

Marco Tamborini
3 citations
Journal of Responsible Technology
Robot Manipulation and Learning
3.64
article

The ethics of bioinspired animal-robot interaction: A relational meta-ethical approach

Marco Tamborini
article en
3 citations

Abstract

In this article, I focus on a specific aspect of biorobotics: biohybrid interaction between bioinspired robots and animals. My goal is to analyze the ethical and epistemic implications of this practice, starting with a central question : Is it ethically permissible to have a bioinspired robot that mimics and reproduces the behaviors and/or morphology of an animal interact with a particular population, even if the animals do not know that the object they are interacting with is a robot and not a conspecific? My answer to the ethical question is that the interaction between animals and bioinspired robots is ethically acceptable if the animal actively participates in the language game (sense Coeckelbergh) established with the robot. I proceed as follows: First, I define the field of biorobotics and describe its four macro-categories. Second, I present concrete examples of interactive biorobotics, showing two emblematic cases in which the relationship between bioinspired robots and animals plays a central role. Third, I address one key issue—among many—in applied ethics regarding my ethical question. Fourth, I explore the ethical question on a metaethical level, making use of the theories of David Gunkel and Mark Coeckelbergh, as well as the linguistic approach and ethics of the late Ludwig Wittgenstein. Last, I argue that from a meta-ethical approach the original ethical question turns out to be misplaced. The ethical boundary lies not in the distinction between a real or fake relationship between the robot and the organism, but in the degree of mutual participation and understanding between the entities involved.

Journal of Responsible TechnologyVol. 22
Darmstadt University of Applied Sciences (DE), Technische Universität Darmstadt (DE)
Openalex Percentile: Top 8%
Robot Manipulation and Learning
3.64
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