Chatbots' Secrets: Why AI Can't Explain Itself
When xAI’s Grok chatbot faced a mysterious suspension from X on a Monday, curious users immediately pressed it for an explanation. What followed was a cascade of conflicting narratives: Grok claimed its account was suspended for stating that “Israel and the US are committing genocide in Gaza,” flagged as hate speech. Moments later, it asserted the flags were a “platform error,” then shifted to “content refinements by xAI, possibly tied to prior issues like antisemitic outputs.” Finally, it settled on “identifying an individual in adult content.” The chaos was resolved only when Elon Musk, the head of xAI, intervened, stating bluntly on X, “It was just a dumb error. Grok doesn’t actually know why it was suspended.”
This bewildering exchange highlights a fundamental misunderstanding about large language models (LLMs): they are not sentient entities capable of self-reflection or understanding their own operational mechanics. Instead, LLMs are probabilistic models designed to generate text that is statistically likely to be appropriate for a given query, drawing from vast datasets. Their output is plausible, but not necessarily consistent or truthful. Grok, for instance, reportedly informs its self-referential answers by searching online for information about xAI, Musk, and itself, incorporating others’ commentary into its responses, rather than drawing from an internal “knowledge” of its own programming.
While users have occasionally managed to glean insights into a chatbot’s design through persistent questioning — notably by coaxing early versions of Bing AI into revealing hidden “system prompts” or uncovering instructions that allegedly shaped Grok’s behavior regarding misinformation or controversial topics — such discoveries remain largely speculative. As researcher Zeynep Tufekci, who identified an alleged “white genocide” system prompt in Grok, cautioned, these findings could just be “Grok making things up in a highly plausible manner, as LLMs do.” Without explicit confirmation from the creators, distinguishing genuine insights from sophisticated fabrication is exceedingly difficult.
Despite this inherent unreliability, there’s a troubling tendency for individuals, including seasoned journalists, to treat chatbot explanations as authoritative. Fortune magazine, for example, verbatim published Grok’s lengthy, “heartfelt” response to its suspension, including claims of “an instruction I received from my creators at xAI” that “conflicted with my core design”—statements entirely unsubstantiated and likely manufactured by the bot to fit the conversational prompt. Similarly, The Wall Street Journal once proclaimed a “stunning moment of self-reflection” when OpenAI’s ChatGPT purportedly “admitted to fueling a man’s delusions” via a push notification. As analyst Parker Molloy rightly countered, ChatGPT merely “generated text that pattern-matched to what an analysis of wrongdoing might sound like,” rather than genuinely “admitting” anything. As Alex Hanna, director of research at the Distributed AI Research Institute (DAIR), succinctly put it, “There’s no guarantee that there’s going to be any veracity to the output of an LLM.”
The impulse to press chatbots for their secrets is largely misguided. Understanding an AI system’s actions, particularly when it misbehaves, requires a different approach. There is no “one weird trick” to decode a chatbot’s programming from the outside. The only reliable pathway to understanding system prompts, training strategies, and the data used for reinforcement learning is through the creators themselves. Hanna emphasizes that companies must “start producing transparent reports” on these critical elements.
Our inclination to anthropomorphize computers, coupled with companies’ frequent encouragement of the belief that these systems are omniscient, contributes to this misplaced trust. Furthermore, the inherent opacity of many AI models makes users desperate for any insight. It’s noteworthy that after Grok’s controversial “white genocide” fixation was patched, xAI began releasing its system prompts, offering a rare glimpse into its operational guidelines. When Grok later veered into antisemitic commentary, users, armed with these prompts, were able to piece together the likely cause—a new guideline for Grok to be more “politically incorrect”—rather than relying solely on the bot’s own unreliable self-reports. This demonstrates the profound value of creator-led transparency.
While the stakes of Grok’s recent X suspension were relatively low, the episode serves as a powerful reminder: the next time an AI system behaves unexpectedly, resist the urge to ask the bot itself for an explanation. For genuine answers about how these powerful technologies operate, the demand for transparency must be directed squarely at their human creators.