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

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.