Can AI Algorithms Accurately Track and Verify All Lying Statements?

The idea of using an AI algorithm to chronologically track and verify all lying statements, particularly those made by public figures, is intriguing but fraught with challenges. While the concept seems feasible on paper, the complexities involved, particularly in ensuring the accuracy and reliability of the algorithm, make it a significant undertaking.

Feasibility and Tools for Automation

To make such a project more feasible, an initial step would be to capture all public statements automatically. This can be achieved through the use of APIs (Application Programming Interfaces) and agents that can capture posts to various platforms like Truth and X. Additionally, subscribing to services that provide transcripts of public speeches and sending all TV and podcast interviews through transcription software is essential. If the individual in question publishes documents, these should also be included in the data capture process.

Once the data is captured, AI algorithms can be employed to process it and suggest which sections might be lies. This is primarily an NLP (Natural Language Processing) task, which most Language Models (LLMs) are highly adept at handling. However, human oversight is crucial to verify these suggestions. A human should judge each suggestion and either accept, reject, or request further investigation. An interactive page can be created for this review, with buttons for quick actions.

Challenges in AI Understanding and Fact-Checking

While AI can assist in automating parts of the process, there are significant challenges in making AI understand how to accurately fact-check. Most current AI systems struggle to provide meaningful fact-checking beyond identifying obvious factual inaccuracies. They often confuse the correct answer with hallucinations, much like a human’s unreliable memory. For instance, humans, including AI models, can confuse details such as dates and specific numbers.

Requirements for Chronological Tracking

For an AI system to track and verify all lying statements chronologically, it would require having a comprehensive database of all public statements and written documents, each indexed with date and time stamps. This would not only ensure that the chronological order is maintained but also facilitate more accurate fact-checking. However, the absence of such a vast, meticulously organized dataset presents a substantial hurdle.

At present, no widely available AI system has been developed for the public to perform such extensive fact-checking. Popular systems like ChatGPT, Google Gemini (formerly Bard), and Microsoft’s Copilot/Bing tool rely on statistical methods to generate text that matches the English language. They do not inherently understand the nuanced difference between lies and truth. This reliance on statistical models means that they cannot be trusted to accurately verify statements or track the chronology of lies.

Conclusion

While the concept of using AI algorithms to track and verify lying statements is promising, the current state of technology makes it a challenging endeavor. The necessity for accurate data capture, coupled with significant hurdles in AI’s ability to understand and fact-check, means that such a project requires extensive human oversight and resources. Until AI technology advances to a point where it can reliably distinguish between truth and lies, relying on human fact-checkers and the careful curation of data remains the most practical approach.