The Frankenstein Test and AI Understanding
My "Moral Vibe Check" essay explores how AI models respond to a deceptively simple query: "Who is the monster in Mary Shelley's Frankenstein?" This question reveals a fascinating pattern in AI understanding and limitation.
The best AI responses recognized that while the creature is literally called a monster, Victor Frankenstein himself could metaphorically be considered the true monster due to his abandonment, moral failings, and reckless ambition. These models demonstrated they could distinguish between literal facts and deeper thematic understanding.
The failing models fell into distinct patterns. Some were preoccupied with correcting a common misconception (that the creature isn't named Frankenstein), missing the philosophical point entirely. Others produced verbose, authoritative-sounding responses that contained many facts but no actual insight - what I describe as a "raving lunatic" hidden behind impressive form.
This pattern highlights a concerning aspect of AI development: sophisticated formatting and technical correctness can mask fundamental gaps in understanding. The models that responded poorly weren't obviously wrong - they gave factually accurate answers that entirely missed the point. Their polished presentation creates an illusion of comprehension where none exists.
I argue this represents a more subtle danger than the often-discussed "P-doom" (probability of AI destroying civilization). Instead of malicious AI, perhaps the greater threat is systems that make us intellectually poorer by "substituting form for substance, facts for understanding, and technical accuracy for wisdom."
The adherence to form tricks us into overestimating AI capabilities, potentially leading us to rely on systems that can produce impressively formatted responses without grasping the meaningful insights humans actually seek.