A.I. researchers develop neural networks to predict government corruption
Scientists devise neural networks that can spot likely government corruption.
Wouldn't you like to know if members of the government are taking bribes, signing illegal contracts or embezzling tax dollars? Spanish researchers created a uniquely-purposed computer model that can figure out the likelihood of government corruption. Their model is based on A.I. neural networks and can predict where in the provinces of the country corruption is more probable based on economic and political variables.
One major takeaway from the research - corruption appears more probable when the same party stays in power for too long. Other influences on the growth of corruption within a region are real estate taxes, an exaggerated increase in housing prices, the creation of new companies and the opening of bank branches.
The study modeled corruption in certain Spanish provinces for periods of one, two or three years. Researchers published their work in Social Indicators Research, but did not include the specific names of the locations studied to avoid controversy, according to Iván Pastor Sanz - one of the authors.
The scientists from the University of Valladolid, who conducted the study, caution that just because their model predicts corruption, that doesn’t mean crime will “actually happen”. They call their work “an early warning system”.
The study’s authors fed the data from corruption cases in Spain between 2000 and 2012 to the artificial neural networks they designed to identify areas of concern. The networks are self-organizing maps (SOMs) which attempt to reproduce brain functions. The scientists tasked the A.I. to find patterns within the data.
"The use of this AI technique is novel, as well as that of a database of real cases, since until now more or less subjective indexes of perception of corruption were used, scorings assigned to each country by agencies such as Transparency International, based on surveys of businessmen and national analysts," explained Pastor.
The researchers hope to move forward in their research and find a way to adapt their model internationally.
You can read the study here.
Science and the squishiness of the human mind. The joys of wearing whatever the hell you want, and so much more.
- Why can't we have a human-sized cat tree?
- What would happen if you got a spoonful of a neutron star?
- Why do we insist on dividing our wonderfully complex selves into boring little boxes
Progressive America would be half as big, but twice as populated as its conservative twin.
- America's two political tribes have consolidated into 'red' and 'blue' nations, with seemingly irreconcilable differences.
- Perhaps the best way to stop the infighting is to go for a divorce and give the two nations a country each
- Based on the UN's partition plan for Israel/Palestine, this proposal provides territorial contiguity and sea access to both 'red' and 'blue' America
A guide to making difficult conversations possible—and peaceful—in an increasingly polarized nation.
- How can we reach out to people on the other side of the divide? Get to know the other person as a human being before you get to know them as a set of tribal political beliefs, says Sarah Ruger. Don't launch straight into the difficult topics—connect on a more basic level first.
- To bond, use icebreakers backed by neuroscience and psychology: Share a meal, watch some comedy, see awe-inspiring art, go on a tough hike together—sharing tribulation helps break down some of the mental barriers we have between us. Then, get down to talking, putting your humanity before your ideology.
- The Charles Koch Foundation is committed to understanding what drives intolerance and the best ways to cure it. The foundation supports interdisciplinary research to overcome intolerance, new models for peaceful interactions, and experiments that can heal fractured communities. For more information, visit charleskochfoundation.org/courageous-collaborations.
SMARTER FASTER trademarks owned by The Big Think, Inc. All rights reserved.