Published By: Tech Times, 10/7/2016
The CIA and their new Directorate of Digital Innovation are working on “anticipatory intelligence” to predict future events. The Deputy Director says that they can sometimes forecast outbreaks of unrest up to five days ahead. These predictions are made by using classified information as well as open source data; commentators speculate that much of the data comes from massive social media surveillance.
Extended Discussion Questions
- The article mentions that the CIA is using open source data, that is, publicly available data sets. Companies such as Google use similar open data sets (e.g., Google maps uses open data sets). What issues could arise from a dependence on these open data sets?
- What benefits does using open data sets provide?
- What are some potential benefits and pitfalls of using “anticipatory intelligence” as an information source for making decisions, for entities such as the FBI, or when making decisions related to international policies and trade arrangements?
Relating This Story to the CSP Curriculum Framework
Global Impact Learning Objectives:
- LO 7.2.1 Explain how computing has impacted innovations in other fields.
- 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 7.3.1G Privacy and security concerns arise in the development and use of computational systems and artifacts.
- EK 7.3.1H Aggregation of information, such as geolocation, cookies, and browsing history, raises privacy and security concerns.
- EK 7.3.1J Technology enables the collection, use, and exploitation of information about, by, and for individuals, groups, and institutions.
Other CSP Big Ideas:
- Idea 3 Data and Information
Banner Image: “Network Visualization – Violet – Crop 10”, 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