The main shortcoming of AI is its lack of intelligence

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)

Content:

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

Applications

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

Forget the ***city

In 2009, IMB launched a global marketing campaign around the previously little-known concept of ‘smart city’ with the aim of making city governments receptive to ICT applications in the public sector. The initial emphasis was on process control. Emerging countries were interested in the first place: Many made plans to build smart cities ‘from scratch’, in the first place to attract foreign investors. The Korean city of Songdo, developed by Cisco and Gale International, is a well-known example. 

The emphasis soon shifted from process control to using data from the residents themselves. Google wanted to supplement its already rich collection of data with data that city dwellers provide with their mobile phones to create a range of new commercial applications. Its sister company Sidewalk Labs, which was set up for that purpose, started developing a pilot project in Toronto. That failed, partly due to the growing resistance to the prospective violation of privacy. This opposition has had global repercussions and resulted in many countries in legislation to protect privacy.

The rapid development of digital technologies, such as artificial intelligence, gave further impetus to discussion about the ethical implications of technology. Especially in the US, applications in facial recognition and predictive police were heavily criticized.

This current situation – particularly in the Netherlands – can be characterized on the one hand by the development of regulations to safeguard ethical principles and on the other by the search for responsible applications of digital technology.

The question is therefore how useful the term ‘smart city’ is. Touria Meliani, alderman of Amsterdam, prefers to speak of ‘wise city’ than of ‘smart city’ to emphasize that she is serious about putting people first. But instead of introducing other adjectives, skipping them all is better.

The best way to understand human life in the city is respecting the complexity of the city and life within it. Exactly because of the city’s complexity, the use of reductionist adjectives such as ‘smart’, ‘sharing’, circular, climate-neutral’, ‘resilient’ is better omitted. The doughnut-principle is the best way to analyze the city from different perspectives and to define the way people can live in a social and ecological sustainable way, the use of digital technology included.

This post based on by the new e-book Better cities, the contribution of digital technology.  Interested? Download the book here for free