PROVE ME WRONG.
Saved in:
| Title: | PROVE ME WRONG. |
|---|---|
| Authors: | HERBST, MEGHAN (AUTHOR) |
| Source: | Wired. Jul/Aug2026, Vol. 34 Issue 4, p46-49. 4p. 4 Color Photographs. |
| Subjects: | Fact checking, Artificial intelligence, Judgment (Psychology), Statistical accuracy, Language models |
| Abstract: | The article examines the current capabilities and limitations of artificial intelligence (AI) in fact-checking, highlighting that while nearly half of Americans use AI for information gathering, AI systems frequently produce inaccurate results—often wrong about 30 to 60 percent of the time according to various studies. Traditional fact-checking, as practiced by human editors at WIRED, involves meticulous verification through primary sources and direct communication, a process AI has yet to replicate effectively. Although AI tools like those developed by the UK’s Full Fact initiative assist in identifying claims for human review, experts emphasize the necessity of human judgment due to AI’s persistent errors and hallucinations. The article also discusses benchmark tests showing that leading large language models (LLMs) achieve accuracy rates generally below 75 percent on fact-checking tasks, and notes that AI’s increasing sophistication does not guarantee fewer factual mistakes. Ultimately, the piece suggests that while AI can support fact-checkers by pointing to authoritative sources, human expertise remains essential for nuanced verification and understanding of complex or offline knowledge. [Extracted from the article] |
| Copyright of Wired is the property of Conde Nast Publications and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
|
Full text is not displayed to guests.
Login for full access.
|
|
| Abstract: | The article examines the current capabilities and limitations of artificial intelligence (AI) in fact-checking, highlighting that while nearly half of Americans use AI for information gathering, AI systems frequently produce inaccurate results—often wrong about 30 to 60 percent of the time according to various studies. Traditional fact-checking, as practiced by human editors at WIRED, involves meticulous verification through primary sources and direct communication, a process AI has yet to replicate effectively. Although AI tools like those developed by the UK’s Full Fact initiative assist in identifying claims for human review, experts emphasize the necessity of human judgment due to AI’s persistent errors and hallucinations. The article also discusses benchmark tests showing that leading large language models (LLMs) achieve accuracy rates generally below 75 percent on fact-checking tasks, and notes that AI’s increasing sophistication does not guarantee fewer factual mistakes. Ultimately, the piece suggests that while AI can support fact-checkers by pointing to authoritative sources, human expertise remains essential for nuanced verification and understanding of complex or offline knowledge. [Extracted from the article] |
|---|---|
| ISSN: | 10591028 |
