Artificial intelligence is the ‘independent construction of pattern in large datasets by computers’. Still, people hold an important role in this. This role consists in the first place in writing an instruction – an algorithm – and then in the composition of a training set, a selection of many examples, for example of animals that are labeled as dog or cat and if necessary, lion or tiger and more. In essence, the computer looks for statistically significant similarities in whatever data, provided by the operator, to predict the probability that some relation will exist or phenomenon will occur.
If computers are learned to make judgement about people, things can go terribly wrong. The St. George Hospital Medical School in London has employed disproportionately many white males for at least a decade because the ‘learning set’ reflected the incumbent staff. The learning set itself represented the bias of those who selected it.
The fight against crime in the United States, has been the scene of artificial intelligence’s abuse for years. The two most used techniques that resulted are predictive policing (PredPol) and facial recognition. In the case of predictive policing, patrols are given directions in which neighborhood or even street they should patrol at a given moment because computers have calculated that the risk of crimes (vandalism, burglary, violence) is highest then.
Predictive policing and facial recognition are based on a “learning set” of thousands of “suspicious” individuals. At one point, New York police had a database of 48,000 individuals. 66% of those were black, 31.7% were Latino and only 1% were white. The composition of this dataset was completely biases, and therefore the computerized ‘decisions’ ware biased too. Even worse is that the final ‘decisions’ made by the computer cannot be explained, and the underlying process is a blackbox. This is a serious ethical issue and the reason why many demand to forbit the application of artificial intelligence.
Bias is not the only thing. In so-called fight against crime the computer calculates on request the chances that crime would happen at a certain time and in a certain place. But if the client exchanged the dominant paradigm of identifying, prosecuting and incarcerating criminals for that of finding potential offenders in a timely manner and giving them the help, they need? A large proportion of those arrested by the police in the US are addicted to drugs or alcohol and severely mentally disturbed. The University of Chicago Data Science for Social Good Program used artificial intelligence to analyze a database of 127,000 people. The aim was to find out, based on historical data, which of those involved was most likely to be arrested within a month. This is not with the intention of hastening an arrest with predictive techniques, but instead to offer them targeted medical assistance. This program was picked up in several cities and in Miami it resulted in a 40% reduction in arrests and the closing of an entire prison.
AI means computer power. Intelligence resides in those who are using this power. Less biased application of artificial intelligence depends on the through-out choice of the connections in the learning sets. These connections must be scientifically validated and approved by scholars with different backgrounds instead of police officers or computer scientists.
This post is based on the new e-book Better cities, the contribution of digital technology. Interested? Download the book here for free (90 pages)
Hardcore: Technology-centered approaches
1. Ten years of smart city technology marketing
2. Scare off the monster behind the curtain: Big Tech’s monopoly
Towards a humancentric approach
3. A smart city, this is how you do it
4. Digital social innovation: For the social good
Misunderstanding the use of data
5. Digital twins
6. Artificial intelligence
Embedding digitization in urban policy
7. The steps to urban governance
8. Guidelines for a responsible digitization policy
9. A closer look at the digitization agenda of Amsterdam
10. Forging beneficial cooperation with technology companies
11. Government: How digital tools help residents regaining power?
12. Mobility: Will MaaS reduce the use of cars?
13. Energy: Smart grids – where social and digital innovation meet
14. Healthcare: Opportunities and risks of digitization
Wrapping up: Better cities and technology
15. Two 100 city missions: India and Europe
Epilogue: Beyond the Smart City