As well established as the practice of intelligence analysis may be, researchers continue to ask elementary questions about what analysis is, how it is done, and how it can be done better.
“Intelligence analysis involves a complex process of assessing the reliability of information from a wide variety of sources and combining seemingly unrelated events. This problem is challenging because it involves aspects of data mining, data correlation and human judgment,” one recent study (pdf) performed for the Office of Naval Research observed.
The study focused on development of computer tools to support the analytical method known as Analysis of Competing Hypotheses (ACH), previously explored by Folker (pdf), among others.
See “Assisting People to Become Independent Learners in the Analysis of Intelligence” by Peter L. Pirolli, Palo Alto Research Center, Inc., Final Report to the Office of Naval Research, February 2006.
Americans are paying too much for almost everything, because the United States has long treated its trucking industry as an artifact to be preserved rather than as an opportunity for innovation.
These ideas aim to advance the detailed policy solutions needed to foster public trust and implement fairness in the adoption of AI across diverse domains, from healthcare and government benefits to rural access, education, and worker protections.
The evidence is clear: algorithmic pay-setting is established in app-based work, and payroll/timekeeping failures show how software can produce systemic wage harm at scale
While a few states have taken steps to implement decision-making mechanisms for certain AI systems, too many leaders are simply accepting narratives about AI’s purported public benefit at face value – jumping to the “how” of AI implementation before thoroughly vetting potential systems and deciding whether they are appropriate to use at all.