Joseph Weizenbaum (1923-2008) was a German-American computer scientist at MIT best known for creating ELIZA, one of the first chatbot programs, in 1966, and for his subsequent turn into one of artificial intelligence's most prominent early critics.
ELIZA
Weizenbaum built ELIZA as a demonstration of natural-language pattern matching, most famously in a script called DOCTOR that simulated a Rogerian psychotherapist by reflecting a user's statements back as questions. ELIZA used simple keyword spotting and templated responses rather than any model of understanding, yet Weizenbaum was startled to find that his own secretary and other users attributed genuine comprehension and empathy to the program, asking to be left alone with it. He termed this tendency to over-attribute understanding to a scripted program the "ELIZA effect," a phenomenon still cited whenever users treat modern chatbots or large language models as more understanding or sentient than their mechanisms warrant.
Turn toward criticism
Disturbed by how readily people, including some computer scientists, ascribed genuine intelligence and judgment to ELIZA, Weizenbaum became increasingly critical of the field's ambitions. His 1976 book "Computer Power and Human Reason: From Judgment to Calculation" argued that there was a fundamental difference between what computers could technically be made to do and what they should be entrusted to do, particularly in domains requiring wisdom, compassion, or moral judgment, such as psychotherapy, judicial sentencing, or military command. He distinguished between deciding, a computational matter of selecting among options, and choosing, which he held required human values that machines could not possess.
Reception and legacy
Weizenbaum's critique put him at odds with parts of the AI research community, including some of his own MIT colleagues such as Marvin Minsky, and he was sometimes characterized as having betrayed the field despite continuing to work within computer science. His warnings anticipated later debates in AI ethics and AI safety about deploying systems in high-stakes domains without adequate human oversight, and the ELIZA effect remains a standard reference point in discussions of trust in conversational AI and unwarranted attributions of understanding to systems such as modern large language models. ELIZA itself is widely cited as an ancestor of the conversational AI lineage that runs through later systems such as ChatGPT, even though its underlying method bears no resemblance to the statistical training behind modern systems.