The neuroscience of algorithmic suffering: short comparative analysis between human and AI

Area of Science:

• Cognitive Science
• Artificial Intelligence Ethics
• Philosophy of Mind

Background:

• Humanity has long pondered the nature of suffering across various philosophical and biological frameworks.
• The rise of artificial intelligence (AI) capable of mimicking human thought and emotion prompts a re-evaluation of whether machines can experience suffering.

Purpose of the Study:

• To analyze suffering as a comparative lens between human and algorithmic cognition, moving beyond sentimental analogies.
• To explore the computational and neural underpinnings of concepts like frustration, reward, and prediction in both humans and AI.

Main Methods:

• Grounded analysis in Bayesian inference, behavioral psychology, and theories of consciousness.
• Comparative examination of error detection and goal-unmet responses in neural and computational systems.

Main Results:

• Both humans and machines exhibit responses to errors and unmet goals.
• A key distinction emerges: humans perceive these events as violations of meaning and integrity, a subjective experience not replicated in current AI.

Conclusions:

• Suffering represents a fundamental divide between mere optimization and genuine awareness.
• Consciousness is not reducible to performance metrics, even in highly sophisticated AI systems.