Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Neural Circuits01:25

Neural Circuits

2.6K
Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...
2.6K
Neurulation01:30

Neurulation

45.1K
Neurulation is the embryological process which forms the precursors of the central nervous system and occurs after gastrulation has established the three primary cell layers of the embryo: ectoderm, mesoderm, and endoderm. In humans, the majority of this system is formed via primary neurulation, in which the central portion of the ectoderm—originally appearing as a flat sheet of cells—folds upwards and inwards, sealing off to form a hollow neural tube. As development proceeds, the...
45.1K
Neural Regulation01:37

Neural Regulation

43.0K
Digestion begins with a cephalic phase that prepares the digestive system to receive food. When our brain processes visual or olfactory information about food, it triggers impulses in the cranial nerves innervating the salivary glands and stomach to prepare for food.
43.0K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same journal

Giving Meaning to Technological Artifacts in One's Life: An Aspect of Personal Autonomy in a Technological Society.

Science and engineering ethics·2026
Same journal

Not Another Grocery List: Proposals for an Effective AI Ethics Implementation.

Science and engineering ethics·2026
Same journal

From Biopiracy to Sustainable Knowledge Governance: Epistemic Justice and the Reconstruction of Resource Sovereignty in the Global South.

Science and engineering ethics·2026
Same journal

Deliberative Lab Communication and the Practice of Ethical Science.

Science and engineering ethics·2026
Same journal

Graduate Students Find Content of Responsible Conduct of Research Coursework Useful.

Science and engineering ethics·2026
Same journal

Discursive Ethics as a Normative Foundation for Integrating Ethics into AI Clinical Decision Support Systems.

Science and engineering ethics·2026

Related Experiment Video

Updated: Jan 10, 2026

Murine Neural Plate Targeting by In Utero Nano-Injection NEPTUNE at Embryonic Day 7.5
10:49

Murine Neural Plate Targeting by In Utero Nano-Injection NEPTUNE at Embryonic Day 7.5

Published on: February 14, 2022

4.5K

Mele's Digital Zygote: Developer Responsibility for Neural Networks.

Anders Søgaard1, Filippos Stamatiou2

  • 1Department of Computer Science, University of Copenhagen, København, Denmark.

Science and Engineering Ethics
|November 26, 2025
PubMed
Summary

Developers are not solely responsible for neural network predictions, as distinguishing foreseeable from unforeseeable outcomes is impossible. This challenges traditional notions of responsibility for both AI and humans, suggesting no technology-specific gap exists.

Keywords:
AccountabilityDeveloper responsibilityEthics of AIMoral responsibilityResponsibility gaps

More Related Videos

Neonatal Subventricular Zone Electroporation
08:06

Neonatal Subventricular Zone Electroporation

Published on: February 11, 2013

13.3K
In ovo Electroporation of miRNA-based Plasmids in the Developing Neural Tube and Assessment of Phenotypes by DiI Injection in Open-book Preparations
12:41

In ovo Electroporation of miRNA-based Plasmids in the Developing Neural Tube and Assessment of Phenotypes by DiI Injection in Open-book Preparations

Published on: October 16, 2012

17.0K

Related Experiment Videos

Last Updated: Jan 10, 2026

Murine Neural Plate Targeting by In Utero Nano-Injection NEPTUNE at Embryonic Day 7.5
10:49

Murine Neural Plate Targeting by In Utero Nano-Injection NEPTUNE at Embryonic Day 7.5

Published on: February 14, 2022

4.5K
Neonatal Subventricular Zone Electroporation
08:06

Neonatal Subventricular Zone Electroporation

Published on: February 11, 2013

13.3K
In ovo Electroporation of miRNA-based Plasmids in the Developing Neural Tube and Assessment of Phenotypes by DiI Injection in Open-book Preparations
12:41

In ovo Electroporation of miRNA-based Plasmids in the Developing Neural Tube and Assessment of Phenotypes by DiI Injection in Open-book Preparations

Published on: October 16, 2012

17.0K

Area of Science:

  • Philosophy of Technology
  • Artificial Intelligence Ethics
  • Legal Philosophy

Background:

  • The development of artificial intelligence (AI), particularly neural networks, raises questions about developer accountability for AI-generated outcomes.
  • Philosophical debates exist regarding a potential 'responsibility gap' introduced by AI, where developers might evade accountability for unpredictable AI behavior.

Purpose of the Study:

  • To analyze whether neural networks create a unique responsibility gap for their developers.
  • To investigate the implications of distinguishing between foreseeable and unforeseeable AI predictions for responsibility assignment.
  • To re-examine classical notions of responsibility in light of AI capabilities and human fallibility.

Main Methods:

  • Conceptual analysis of responsibility assignment in the context of neural network predictions.
  • Examination of empirical facts regarding the predictability of neural network outputs.
  • Revisiting and reinterpreting established philosophical and legal cases (e.g., Mele's Zygote, Palsgraf) to draw parallels with AI responsibility.

Main Results:

  • Empirical evidence suggests it is impossible to reliably distinguish between foreseeable and unforeseeable neural network predictions.
  • This indistinguishability forces a dilemma: developers must assume full responsibility or none, potentially creating a gap.
  • However, the same empirical challenges in prediction also apply to human actions, suggesting the issue lies with classical responsibility concepts, not AI itself.

Conclusions:

  • There is no unique 'technology-induced' responsibility gap for AI developers.
  • The complexities in assigning responsibility for neural networks mirror those found in assigning responsibility for human actions.
  • The study suggests a need to revise classical notions of responsibility to account for inherent unpredictability in complex systems, both artificial and human.