AI misinformation detectors can’t save us from tyranny—at least not yet
Bibliographic Data
| ID | 21428274 |
|---|---|
| Authors | Walter J Scheirer (0000-0001-9649-8074, corresponding author) |
| Year | 2024 |
| Volume | 80 |
| Issue | 5 |
| Pages | 308-313 |
| Publication date | 2024-09-02 |
| Open Access | No |
| Type | ARTICLE |
| Venue | Bulletin of the Atomic Scientists (JOURNAL) |
| Journal identifiers | ISSN: 0096-3402 • E-ISSN: 1938-3282 |
| Publisher | Informa UK Limited (PUBLISHER • GB) |
| DOI | 10.1080/00963402.2024.2388467 |
| OpenAlex | W4402284462 |
| Language | EN |
| References cited | 9 |
AI-powered misinformation detectors—artificial intelligence tools that identify false or inaccurate online content—have emerged as a potential intervention for helping internet users understand the veracity of the content they view. However, the algorithms used to create these detectors are experimental and largely untested at the scale necessary to be effective on a social media platform. Furthermore, the assumptions programmed into these algorithms frequently conflict with everyday, non-threatening online communication, potentially leading to unwarranted censorship. Consequently, understanding the current limitations of AI interventions for misinformation detection on the internet is important for developing better iterations of these tools and forming effective policies around them
Computer security · Detector · Internet privacy · Misinformation · Political science · Telecommunications · Adversarial Robustness in Machine Learning · Anomaly Detection Techniques and Applications · Computer Science · Digital Media Forensic Detection
| Highly cited | No |
|---|