Published By: MIT Technology Review, 11/14/2016
Researchers from Columbia University and a Brazilian think tank have developed a machine-learning algorithm to predict whether now-declassified U.S. State Department cables from the 1970’s were unclassified, limited official use, confidential, or secret, based on contents and metadata such as sender and recipient. The study provides insight into how the information was classified, but also carries potential national security implications by highlighting trends in erroneous information classification — and systematic gaps where secret cables should have been declassified.
Extended Discussion Questions
- How might more consistent and reliable classification of U.S. diplomatic information impact you?
- How might other countries use the information about how reliable the U.S. government’s classification systems are (or at least were)?
- Do you think the researchers acted responsibly in performing this study? In making the results public?
- The article mentions errors (or gaps) where original cables were not converted to electronic format. What does this say about the reliability of information originally encoded in an outdated format? Can you think of other forms of media that could be compromised due to the rapid development of computing?
Relating This Story to the CSP Curriculum Framework
Global Impact Learning Objectives:
- LO 7.3.1 Analyze the beneficial and harmful effects of computing.
- LO 7.4.1 Explain the connections between computing and real-world contexts, including economic, social, and cultural contexts.
Global Impact Essential Knowledge:
- EK 7.1.1F Public data provides widespread access and enables solutions to identified problems.
- EK 7.3.1A Innovations enabled by computing raise legal and ethical concerns.
- EK 7.3.1E Commercial and governmental censorship of digital information raise legal and ethical concerns.
Other CSP Big Ideas:
- Idea 3 Data and Information
- Idea 4 Algorithms
Banner Image: “Network Visualization – Violet – Offset Crop”, derivative work by ICSI. New license: CC BY-SA 4.0. Based on “Social Network Analysis Visualization” by Martin Grandjean. Original license: CC BY-SA 3.0