Discover how Askonomy, an AI-powered chatbot, combats chatbot hallucination using verified data, human oversight, and ethical design to restore trust in journalism.
Introduction: When Chatbot Hallucination Becomes a Global Challenge
Every day, millions of words appear not through human effort but through machine. Generative AI has unlocked unprecedented creativity and productivity, yet it has also unleashed a darker paradox: the era of chatbot hallucination.
As deep-learning systems generate vast amounts of convincing text, the boundary between accurate financial information and machine-fabricated facts grows dangerously thin. In fact, recent studies show that major chatbots like ChatGPT or Gemini occasionally hallucinate economic data or misquote financial indicators, due to training on unverified or outdated sources.
From fabricated GDP numbers to misinterpreted policy statements, hallucination has shifted from a technical flaw into a credibility crisis. The question now arises: “Can we use the same AI technology that hallucinates to correct itself?”
That’s exactly where Askonomy, an AI-powered economic information assistant, steps in as a corrective force.
The New Face of Chatbot Hallucination in the Digital Era
From Empowerment to Error: When AI’s Potential Is Misused
AI has reshaped how information is created and distributed. Large language models now help journalists summarize reports, detect bias, and personalize news delivery. Visual AI tools assist editors in verifying imagery and archiving millions of records. These advances have made the news ecosystem faster and smarter.
Yet the same technology also amplifies inaccuracy when used carelessly. Generative models, when misused or trained on unstructured web data, can manufacture chatbot hallucination at scale, confidently producing wrong or unverifiable information. What began as a tool for creative acceleration has evolved into an instrument capable of manipulating economies, reputations, and even governance.
Why Chatbot Hallucination Threatens Journalism
The rise of chatbot hallucination poses two critical challenges.
First, the sheer velocity of misinformation: Generative models can draft thousands of articles or social posts within minutes. This speed outpaces traditional fact-checking workflows. The Brennan Center noted that AI misinformation did not decisively affect the 2024 U.S. election, but it warned that “the structural conditions for future harm are firmly in place.”
Second, credibility erosion: Audiences quickly lose trust when they suspect that content comes from a machine without clear sources One study show that simply labeling content as AI-generated does little to improve perceived accuracy or reduce sharing.
These risks are not theoretical, they’re measurable:
- Financial fraud: Deepfake scams in North America surged 1,700% from 2022–2025, causing $200M+ in early 2025 losses as fake executive videos tricked employees into fund transfers.
- Market manipulation: AI-generated “news” about bank collapses in late 2025 triggered panic selling and 2–3% swings in the Dow Jones, prompting SEC scrutiny over AI use in financial communications.
- Reputation attacks: Fabricated CEO confession videos led to boycotts and stock declines; The Guardian and Trustpilot (2025) reported 89% of consumers avoid brands linked to misinformation.
This is where solutions like Askonomy become crucial: not to suppress AI, but to align it with truth, transparency, and human oversight, especially in financial contexts were precision matters most.
Askonomy: A Journalistic Answer to AI Hallucination
As the threat of chatbot hallucination accelerates, the question for journalism is no longer “How fast can we detect it?” but “How can we rebuild trust and context in the age of generative media?” This is where Askonomy steps in, an AI powered conversational research system designed to assist journalists, editors, and readers in navigating vast data landscapes with verified insight.
Developed by Trung Huynh, PhD – Head of AI at XNOR Group, in collaboration with VNEconomy, Askonomy is not just another chatbot. It represents a shift from passive information retrieval to active, contextual dialogue, an AI that reads, connects, and reasons like an investigative assistant.
From Reading to Conversing
Traditional journalism relies on human capacity to read, compare, and synthesize tasks that AI can now augment, not replace. Askonomy transforms articles, reports, and data archives into interactive knowledge maps, allowing users to ask complex questions such as:
“How have Vietnam’s fintech regulations evolved since 2020?”
“What macroeconomic trends correlate with retail investor sentiment?”
Instead of generic summaries, Askonomy provides context-aware responses, citing verified sources and showing linkages across data points. This conversational approach doesn’t just deliver answers; it restores the chain of reasoning that audiences can trust, a critical defense against the growing tide of AI fake news.
A Clean Data Approach
At the core of Askonomy is a structured knowledge base built from curated, reputable media sources, peer-reviewed reports, verified press releases, and official statistics. Unlike general-purpose models trained on uncontrolled internet data, Askonomy prioritizes data cleanliness, traceability, and bilingual accuracy, essential for markets like Vietnam where news often spans both local and English contexts.
By maintaining this clean-data pipeline, the system minimizes hallucination, a common problem behind many AI fake news incidents. In doing so, Askonomy turns responsible data governance into a competitive advantage for ethical journalism.
The Human Element: Fighting Chatbot Hallucination with Human Ethics
As AI takes on a growing role in information creation, the ethical responsibility for what it produces remains firmly human. The spread of AI fake news has revealed a critical truth: technology alone cannot distinguish credibility from manipulation; it mirrors the intent of its designers and the data it consumes.
In responsible AI journalism, human oversight is not a safeguard of the past but a structural necessity. Editorial teams, data curators, and AI engineers must collaborate to define boundaries: what sources are trustworthy, what content should be excluded, and how transparency is maintained when AI summarizes or rephrases journalistic work.
Emerging frameworks, such as human in the loop verification and ethical data governance, are helping media organizations worldwide ensure accountability in AI assisted reporting. These practices combine the scalability of automation with the moral reasoning and contextual judgment that only humans can provide.
Askonomy embodies this principle through its development approach by Trung Huynh, PhD – Head of AI at XNOR Group, with VNEconomy. Its design reflects a belief shared by many in the AI ethics community: that credible information ecosystems require not just intelligent machines but human values embedded at every layer of interaction.
Ultimately, the fight against AI fake news is not a war of algorithms but a collective rethinking of trust, how it is built, maintained, and restored in the age of machine generated truth.
Conclusion: Turning the Chatbot Hallucination Crisis into an Opportunity for Truth
The rise of chatbot hallucination represents one of the defining ethical and informational challenges of our time. Yet, within this disruption lies a rare opportunity to redefine journalism itself for the AI age.
Forward-thinking media and technology leaders now treat generative AI as a mirror. It reveals weaknesses in our information systems and encourages us to rebuild them with stronger ethical foundations.
Projects like Askonomy, embody this shift in perspective. With verified data, human oversight, and transparent design, AI can amplify truth rather than distort it.
In this new paradigm, truth becomes both the mission and the innovation. The future of journalism will not revolve around faster content production or more complex algorithms. It will depend on systems that earn and keep public trust.
