![\"Why](\"https:\/\/static.toiimg.com\/thumb\/msid-131932481,imgsize-1669149,width-400,height-225,resizemode-4\/131932481.jpg\" "\\"(Representative")
<\/div>\n<\/div>\n<\/div>\n<\/section>\n<\/div><\/div>\n<\/div>\nHallucinations have recently been used to refer to a typical behavioral pattern that Generative Artificial Intelligence (AI) models exhibit.AI hallucinations occur when a model generates a completely fabricated response. The plausible yet false answers have no ground in reality, or training data.The most peculiar aspect of these hallucinations is the tone: the response, though misleading and invented, sounds completely confident. These responses show no shred of uncertainty, which often makes users inclined to believe the misinformation.From recommending users put glue on their pizza to making up summer reading lists of books that do not exist (which made its way to a newspaper! ), AI hallucinations have been the subject of internet virality innumerable times over the past few years.This strange phenomenon warrants dissection.<\/p>\n WHAT ARE AI HALLUCINATIONS<\/p>\n<\/div>\n<\/div>\n Why AI models hallucinate<\/span>Language models hallucinate because standard training and evaluation procedures reward guessing over acknowledging uncertainty, suggests a research paper published by scholars at OpenAI titled: \u2018Why language models hallucinate.\u2019<\/span>The paper explains it with an analogy: the multiple-choice question. A wild guess on an unknown question might be correct if luck is on one\u2019s side. Leaving it blank on the other hand? That guarantees a zero. Large Language Models (LLMS) are graded similarly, based on accuracy and the percentage of questions they get right.This current evaluation method sets the wrong incentive, one that encourages guessing rather than an honest expression of uncertainty.Furthermore, language models respond to prompts by predicting one word at a time. It is simply a question of probability and patterns. Mistakes are inevitable in such a process.<\/p>\n WHY AI MODELS HALLUCINATE<\/p>\n<\/div>\n<\/div>\n Worrisome patterns<\/span>In October 2024, Associated Press published an investigation on the usage of OpenAI\u2019s transcription tool Whisper in hospitals. It said that despite being pushed as having \u201chuman level robustness and accuracy,\u201d Whisper was often prone to making up chunks of text and even entire sentences that were never said in the first place.Despite OpenAI\u2019s warning about not using Whisper in \u201chigh-risk domains,\u201d it was found to be used to transcribe doctor-patient consultations anyway, as it enabled medical providers to save time on report writing and note taking.\u201cThose experts (more than a dozen software engineers, developers and academic researchers) said some of the invented text \u2014 known in the industry as hallucinations \u2014 can include racial commentary, violent rhetoric and even imagined medical treatments,\u201d stated the report.The issue with AI-powered transcriptions is not just fabrication, but also bias. In the words of American linguist Mary Bucholtz, \u201cAll transcripts take sides, enabling certain interpretations, advancing particular interests, favoring specific speakers.\u201dIn May 2026, the accounting firm Ernst & Young (EY) retracted a report flagged for citation errors, fake footnotes and statistics suspected to be hallucinated by AI. EY Canada\u2019s cybersecurity report on loyalty program safeguards had referred to studies that simply did not exist, revealed an investigation by GPTZero, an AI-detection startup.The report titled \u2018Chasing the hallucinations\u2019 suggested how contradicting references, low-quality sources and out-of-date statistics are all AI indicators.<\/span> The report ended with a warning: \u201cFake information poisons the well and misleads future researchers, especially when published by a major consulting firm. Claude, ChatGPT, Perplexity all surface hallucinations from EY\u2019s flawed report.\u201d<\/p>\n WHEN AI FAKES ITS SOURCES<\/p>\n<\/div>\n<\/div>\n How AI hallucinations affect academics, researchers<\/span>\u201cOn a number of occasions when I asked for references of books or Supreme Court<\/a> cases, AI hallucinated and confidently provided incorrect citations which initially appeared accurate. But when I started checking from original sources, I found them inaccurate,\u201d said Prof (Dr) Anand Pradhan of Indian Institute of Mass Communication. \u201cA few times, it also confused the explanations of particular theories with the work of other scholars.\u201dResearchers at GPTZero coined the term \u2018vibe citing\u2019 to refer to how generative AI models create citations or academic references that appear to be credible but are either misattributions or complete fabrications.They have classified this phenomenon into three distinct patterns, where human errors are exempt:<\/p>\n Hallucinated citations not only call the integrity of the concerned work into question but also serve as a threat to the future of academia and research as a whole.A Master\u2019s student speaks of persistent unease caused by the unreliability of AI-generated academic responses. \u201cGemini links the websites from which it generates its summaries. However, there have been so many times when I\u2019ve tried to access the link but it ended up not being open source so I couldn\u2019t fact-check. Other times, I see the website saying something completely different,\u201d said Ushasi Chowdhury, who is pursuing a Master\u2019s degree in English literature at Loreto College, Kolkata.Ushasi said that whenever a concept in literature or philosophy needs to be referenced, a simple Google<\/a> search helps find websites for a quick and comprehensive understanding. Now that Google provides an AI-generated overview, sometimes it is convenient to read it from there, especially when there is a rush.\u201cConstantly referring to 5 different books is a tedious process, sometimes I end up just going by what the Google AI-summary says,\u201d said the literature student. \u201cIt cannot be a blind trust but I am presented with an illusion of reassurance during circumstances where I don\u2019t have time to look through a lot of sources.\u201dDr Pradhan said that AI seems to be gradually improving accuracy. Even so, it is not yet 100% accurate and its responses still need to be verified, especially in journalism, academic and research contexts, warns the professor.<\/p>\n HOW TO PROTECT YOURSELF<\/p>\n<\/div>\n<\/div>\n Questioning and verification in the age of AI<\/span>It is established that any and all Artificial Intelligence models will sometimes confidently state falsehoods. At the same time, regarding convenience and haste, AI is invincible and cannot be done away with, says a growing consensus.Tools of media literacy prove useful in this predicament.What can help one mitigate the inaccuracy and misinformation is simply a process of vigorous, thorough fact-checking and verification. Not to trust any response given by an AI model blindly, no matter how plausible it sounds or how authoritatively the information it expressed\u2014even, and especially, if it supports one\u2019s preexisting notions.<\/div>\n![\"WHAT](\"https:\/\/static.toiimg.com\/photo\/msid-131933961\/what-are-ai-hallucinations.jpg\" "\\"WHAT")
<\/p>\n![\"WHY](\"https:\/\/static.toiimg.com\/photo\/msid-131934015\/why-ai-models-hallucinate.jpg\" "\\"WHY")
<\/p>\n![\"WHEN](\"https:\/\/static.toiimg.com\/photo\/msid-131934080\/when-ai-fakes-its-sources.jpg\" "\\"WHEN")
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![\"HOW](\"https:\/\/static.toiimg.com\/photo\/msid-131934041\/how-to-protect-yourself.jpg\" "\\"HOW")
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