Greenbaum, Gerstein: Can AI developers avoid Frankenstein’s fateful mistake?

Audiences already know the story of Frankenstein The gothic novel adapted dozens of times the greater part lately in director Guillermo del Toro s haunting revival now available on Netflix is embedded in our cultural DNA as the cautionary tale of science gone wrong But popular heritage misreads author Mary Shelley s warning The lesson isn t don t create dangerous things It s don t walk away from what you create This distinction matters The fork in the road comes after creation not before All powerful technologies can become destructive the choice between outcomes lies in stewardship or abdication Victor Frankenstein s sin wasn t only bringing life to a grotesque creature It was refusing to raise it insisting that the consequences were someone else s dilemma Every generation produces its Victors Ours work in artificial intelligence In the past few days a California appeals court fined an attorney after of circumstance citations in their brief proved to be AI fabrications nonexistent precedents Hundreds of similar instances have been documented nationwide growing from a limited cases a month to a sparse cases a day This summer a Georgia appeals court vacated a divorce ruling after discovering that of citations were AI fabrications How multiple more went undetected ready to corrupt the legal record The obstacle runs deeper than irresponsible deployment For decades computer systems were provably correct a pocket calculator can consistently offer users the mathematically correct answers every time Engineers could demonstrate how an algorithm would behave Failures meant implementation errors not uncertainty about the system itself Modern AI changes that paradigm A latest examination announced in Science confirms what AI experts have long known plausible falsehoods what the industry calls hallucinations are inevitable in these systems They re trained to predict what sounds plausible not to verify what s true When confident answers aren t justified the systems guess anyway Their training rewards confidence over uncertainty As one AI researcher quoted in the record put it fixing this would kill the product This creates a fundamental veracity obstacle These systems work by extracting patterns from vast training datasets patterns so numerous and interconnected that even their designers cannot reliably predict what they ll produce We can only observe how they genuinely behave in practice sometimes not until well after damage is done This unpredictability creates cascading consequences These failures don t disappear they become permanent Every legal fabrication that slips in undetected enters databases as precedent Fake healthcare advice spreads across fitness sites AI-generated news circulates through social media This synthetic content is even scraped back into training records for future models In the modern day s hallucinations become the next morning s facts So how do we address this without stifling innovation We already have a model in pharmaceuticals Drug companies cannot be certain of all biological effects in advance so they test extensively with bulk drugs failing before reaching patients Even approved drugs face unexpected real-world problems That s why continuous monitoring remains essential AI necessities a similar framework Responsible stewardship the opposite of Victor Frankenstein s abandonment requires three interconnected pillars First prescribed training standards Drug manufacturers must control ingredients document production practices and conduct quality testing AI companies should face parallel requirements documented provenance for training records with contamination monitoring to prevent reuse of problematic synthetic content prohibited content categories and bias testing across demographics Pharmaceutical regulators require transparency while current AI companies need to disclose little Second pre-deployment testing Drugs undergo extensive trials before reaching patients Randomized controlled trials were a major achievement developed to demonstrate safety and efficacy Preponderance fail That s the point Testing catches subtle dangers before deployment AI systems for high-stakes applications including legal research biological advice and financial management need structured testing to document error rates and establish safety thresholds Third continuous surveillance after deployment Drug companies are obligated to track adverse events of their products and summary them to regulators In turn the regulators can mandate warnings restrictions or withdrawal when problems emerge AI necessities equivalent oversight Why does this need regulation rather than voluntary compliance Because AI systems are fundamentally different from traditional tools A hammer doesn t pretend to be a carpenter AI systems do projecting authority through confident prose whether retrieving or fabricating facts Without regulatory requirements companies optimizing for engagement will necessarily sacrifice accuracy for region share The trick is regulating without crushing innovation The EU s AI Act shows how hard that is Under the Act companies building high-risk AI systems must document how their systems work assess risks and monitor them closely A small startup might spend more on lawyers and paperwork than on building the actual product Big companies with legal teams can handle this Small teams can t Pharmaceutical regulation shows the same pattern Post-market surveillance prevented tens of thousands of deaths when the FDA discovered that Vioxx an arthritis medication prescribed to more than million patients worldwide doubled the pitfall of heart attacks Still billion-dollar regulatory costs mean only large companies can compete and beneficial treatments for rare diseases perhaps best tackled by small biotechs go undeveloped Graduated oversight addresses this matter scaling requirements and costs with demonstrated harm An AI assistant with low error rates gets extra monitoring Higher rates trigger mandatory fixes Persistent problems Pull it from the territory until it s fixed Companies either improve their systems to stay in business or they exit Innovation continues but now there s more accountability Responsible stewardship cannot be voluntary Once you create something powerful you re responsible for it The question isn t whether to build advanced AI systems we re already building them The question is whether we ll require the careful stewardship those systems demand The pharmaceutical framework prescribed training standards structured testing continuous surveillance offers a proven model for critical technologies we cannot fully predict Shelley s lesson was never about the creation itself It was about what happens when creators walk away Two centuries later as Del Toro s adaptation reaches millions this month the lesson remains urgent This time with synthetic intelligence rapidly spreading through our society we might not get another chance to choose the other path Dov Greenbaum is professor of law and director of the Zvi Meitar Institute for Legal Implications of Emerging Technologies at Reichman University in Israel Mark Gerstein is the Albert L Williams Professor of Biomedical Informatics at Yale University They wrote this column for the Los Angeles Times Related Articles Other voices Cyberattacks are up So why are US defenses down Noah Feldman Why isn t anyone stopping ICE Abby McCloskey The gender wars are heating up on the right David French Trump has put the military in an impossible situation Trudy Rubin As Ukraine falters Trump tries to hand the country to Putin with a shamefully pro-Russia peace plan