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Heb jij weleens nagedacht over hoe de stad eruit zou zien zonder schoenmakers, houtbewerkers en automonteurs?
Van grote industrieën tot kleine werkplaatsen, makerschap is altijd een belangrijk onderdeel geweest van Amsterdam Noord. Verspreid door het stadsdeel vind je individuele makers en collectieven, ambachtslieden en creatieve ondernemers. Hun toekomst in de stad staat, mede door gentrificatie, onder druk. Betaalbaar onderkomen wordt schaars en makers worden de stad uit gedreven.Tegelijkertijd wordt de stad steeds meer afhankelijk van deze makers voor uitdagingen zoals de energietransitie en de enorme vraag naar huisvesting.
Het goede nieuws is dat er in Amsterdam, en specifiek Noord, nog steeds veel makers zijn gevestigd. Wie zijn deze makers van Noord, wat maken ze, en hoe draagt dit bij aan de stad, de buurt, en ons leven?
De tentoonstelling Makers van Noord nodigt bezoekers uit om mee te praten en denken over hedendaags en historisch makerschap in het stadsdeel. Bezoekers kunnen eigen ervaringen in het gebied - van nu en vroeger - achterlaten en hun persoonlijke verhalen delen over vakmanschap en de mensen die hen inspireren.
De tentoonstelling Makers van Noord is van 18 juni t/m 27 augustus te zien in Museum Amsterdam Noord. In de maanden juli en augustus vinden publieksevenementen plaats. Voor meer informatie kijk op: www.waag.org/makers
Openingstijden & locatie
Donderdag t/m zondag, van 13:00 tot 17:00 uur.
How can a modern multinational company transition its value chain to preserve nature and biodiversity rather than deplete it?
Metabolic, WWF-France and the Science-Based Targets Network have worked with Bel to understand what areas to target and where the most positive impacts on biodiversity could be made.
Bel will share their best practices, helping other companies to see which methods work overtime. Together with SBTN, we invite more companies to join the effort.
Ben je een jongprofessional en wil je alles leren over de slimme stad? Meld je nu aan voor de Summerschool ‘Dilemma’s van de slimme stad’ op 24, 25 en 26 augustus in Den Haag. Samen met 24 studenten en jongprofessionals werk je in multidisciplinaire teams en bedenk je een vernieuwend concept voor de randvoorwaarden van de digitale transitie.
Deze summerschool wordt georganiseerd door Future City Foundation en het Kennislab voor Urbanisme, in opdracht van Provincie Zuid-Holland en het Ministerie van Binnenlandse Zaken & Koninkrijksrelaties en in samenwerking met de gemeente Den Haag en LivingLab Scheveningen.
Data: 24, 25 en 26 augustus 2022
Locaties: Den Haag – Scheveningen
Deelname gratis (inclusief overnachtingen en eten en drinken).
Toeslagenaffaire, Chinese camera's of discriminerende AI. Digitalisering en nieuwe technologie stelt ons voor ingewikkelde dilemma's. Hoe willen we deze nieuwe technologie benutten, onder welke voorwaarden? Ga deze zomer mee naar Living Lab Scheveningen, waar smartcityoplossingen in de praktijk worden getest. Tussen de bewoners en bezoekers van Scheveningen. Tijdens de summerschool gaan we met hen in gesprek over de dilemma’s van slimme oplossingen en bedenk je samen met 24 studenten en jongprofessionals een vernieuwd concept voor de randvoorwaarden van de digitale transitie.
Laat je inspireren door experts van de Provincie Zuid-Holland, het Ministerie van Binnenlandse Zaken en Koninkrijksrelaties (BZK), het Living Lab Scheveningen en vele anderen. Ontdek wat mensen bezighoudt en vertaal dat in een concreet product. Aan het einde van de summerschool presenteer je met je team jullie briljante plan aan de vakjury en wie weet winnen jullie die 2000 euro!
Je bent geschikt als je begrijpt dat er voor ingewikkelde problemen geen eenvoudige antwoorden volstaan. En soms ook wel. Wij zijn blij met omdenkers en dwarskijkers.
Je bent tussen de 18 en 29 jaar, je volgt een hbo- of wo-opleiding of bent net aan het werk (max 3 jaar werkervaring). En je hebt een achtergrond in:
- ORGANISATIE: rechten, bestuurderskunde, economie
- SOCIAAL: sociologie, sociale geografie
- RUIMTE: ruimtelijke ordening, geografie, planologie, stedenbouw
- DESIGN: product design, multimedia, marketing
- TECH: geo en media design, game design, computer science / IT en software developing
- of iets anders interessants, want verder out-of-the-box is ook welkom.
Aan deze summerschool kunnen 24 studenten en jongprofessionals deelnemen. Deelnemen is GRATIS (inclusief overnachtingen en eten en drinken).
If you are a user of voice assistants, you have probably experienced misunderstandings, especially if you are a woman or have an accent. One of the reasons behind misunderstandings is bias in voice biometrics. This is not a problem if you’re a white US male, but for the rest of us, voice-based systems may be biased.
If you use Google Assistant, whether you use the Assistant App, use Google Home, or Google Nest, you can help mitigate bias in voice biometrics by donating your voice records.
In the process, you will be able to explore your data before committing to donate it.
To do so:
- Download a copy of your Google Assistant records (See detailed instructions)
- Upload your data to our Data Donation Platform and explore it
- Decide what exactly you want to share and donate your data!
We will use the donated data to develop design guidelines and construct an evaluation dataset that reflects real-life usage conditions of voice biometrics technology. In addition, you can participate in a follow-up interview to delve into your personal experience with voice assistants and further explore your data.
This project is part of my PhD research and your contribution would be greatly appreciated! Feel free to reach out :)
Learn from a diverse group of academics and professionals who are mavericks in their field. They bring their real world insights and expertise into the room providing access to the most up to date impact strategies and systemic change. Established founders and innovative business owners of top agencies, companies and startups will guide you.
In this session, DRIFT will share the process to stimulate transformative innovation. In this interactive masterclass, you will not only learn from theory, but also experience the method yourself and contribute. After this workshop you will understand how societal systems change work, and how you can stimulate transformative innovation.
- Time: 13:00 – 17:00
- Expert: Igno Notermans from DRIFT
- For who: Expand members (included) | Non-members and Explore members pay €299,95 per ticket
About the Urban Living Lab Summit
The 4th Urban Living Lab Summit will again strengthen the urban living lab community by sharing insights and knowledge about the urban living lab methodologies and experiences.
This year’s Summit will work on four leading themes: The Innovation Arena, Scoping and Scaling, Crossing Boundaries and Breaking Barriers. Find the full day program below, filled with presentations, workshops, living lab tours and open discussions. And don’t forget to claim your seat as in-person tickets are limited.
Get your ticket here:
Living Lab Essentials
Living labs have proven to be a successful approach for local and regional innovation, while at the same time aiming for co-creation and learning to make a lasting change and create knowledge for further expanding of urban innovative solutions. To enable new participants to gain basic knowledge on Urban living Labs, we also offer an Urban Living Lab mini-training. So, are you starting your own living lab, or are you getting involved? Are you just interested in ways how to experiment in an urban context? This Urban Living Lab pre-summit is something for you. For more information and registration, click here.
This pre-summit event is Monday, June 20 2022 from 13:00 to 16:30 CET time.
More info and tickets
The ULL Summit will be held in a hybrid format. In this way, you can participate to this event IN-PERSON at the AMS-Institute (limited tickets available)! Or do you prefer the online setting or is travelling to Amsterdam not an option? You can also join ONLINE. Both options will give you access to the presentations, workshops. Certain activities such as tour will not be available online, but we do our best to make it a worthy experience.
Get your ticket here:
Hoe versterken we innovatie in de metropoolregio Amsterdam?
Veel lokale en bevlogen ondernemers willen met grote plannen bijdragen aan de duurzame, circulaire en sociale wereld van morgen. Zij zetten zich in voor energiebesparing, circulaire productie of slimme bereikbaarheid. Maar dragen ze daarmee bij aan een leefbare, bruisende en ondernemende metropool? Gaan we door hun oplossingen zorgvuldiger om met grondstoffen en versterken zij de innovatie in onze stad? Daarover gaan we in gesprek tijdens deze Amsterdam Smart City Meet-up. Samen met innovatieve ondernemers onderzoeken wat er goed gaat in de metropoolregio Amsterdam, maar ook hoe wij beter samen kunnen werken.
Tijdens dit programma gaan we op zoek naar innovatie in de metropoolregio Amsterdam. Moderator Servaz van Berkum (Pakhuis de Zwijger) gaat samen met Leonie van den Beuken (Amsterdam Smart City) en Marije Poel (Hogeschool van Amsterdam) in gesprek met verschillende ondernemers uit de metropoolregio Amsterdam. De ondernemers te gast zijn: Peter Schouten (Soci.bike), Thijs Muizelaar (Innovactory), Max Dijkstra (Reefsystems), Reinier Mommaal (Cirotex), Josephine Nijstad (Caffe Inc) en Almer van der Stoel (Bamboeder). In het tweede uur gaan we in gesprek met de zaal, over de ondernemers, maar ook over het belang van het netwerk van Amsterdam Smart City.
Het programma is enkel fysiek te bezoeken in Pakhuis de Zwijger. Het programma is wel later online terug te kijken via dezwijger.nl/terugkijken of via het YouTube-kanaal van Pakhuis de Zwijger.
Meld je aan via:
In the last episode of the Better cities: The contribution of digital technology-series, I will answer the question that is implied in the title of the series, namely how do we ensure that technology contributes to socially and environmentally sustainable cities. But first a quick update.
Smart city, what was it like again?
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 (see episode 3). Especially emerging countries were interested. Many made plans to build smart cities 'from scratch', also meant to attract foreign investors. The Korean city of Songdo, developed by Cisco and Gale International, is a well-known example. The construction of smart cities has also started in Africa, such as Eko-Atlantic City (Nigeria), Konzo Technology City and Appolonia City (Ghana). So far, these cities have not been a great success.
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 provided 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 violation of privacy. This opposition has had global repercussions and led in many countries to legislation to better protect privacy. China and cities in Southeast Asia - where Singapore is leading the way - ignored this criticism.
The rapid development of digital technologies, such as artificial intelligence, gave further impetus to discussion about the ethical implications of technology (episodes 9-13). Especially in the US, applications in facial recognition and predictive police were heavily criticized (episode 16). Artificial intelligence had meanwhile become widespread, for example to automate decision-making (think of the infamous Dutch allowance affair) or to simulate urban processes with, for example, digital twins (episode 5).
This current situation - particularly in the Netherlands - can be characterized on the one hand by the development of regulations to safeguard ethical principles (episode 14) and on the other by the search for responsible applications of digital technology (episode 15). The use of the term 'smart city' seems to be subject to some erosion. Here we are picking up the thread.
The dozens of descriptions of the term 'smart city' not only vary widely but they also evoke conflicting feelings. Some see (digital) technology as an effective means of urban growth; others see it as a threat. The question is therefore how useful the term 'smart city' is still. 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. According to her, the term 'smart city' mainly emphasizes the technical approach to things. She is not the first. Previously, Daniel Latorre, place making specialist in New York and Francesco Schianchi, professor of urban design in Milan also argued for replacing 'smart' with 'wise'. Both use this term to express that urban policy should be based profoundly on the wishes and needs of citizens.
Whatever term you use, it is primarily about answering the question of how you ensure that people - residents and other stakeholders of a city - are put in the center. You can think of three criteria here:
1. An eye for the impact on the poorest part of the population
There is a striking shift in the literature on smart cities. Until recently, most articles focused on the significance of 'urban tech' for mobility, reduction of energy use and public safety. In a short time, much more attention has been paid to subjects such as the accessibility of the Internet, the (digital) accessibility of urban services and health care, energy and transport poverty and the consequences of gentrification. In other words, a shift took place from efficiency to equality and from physical interventions to social change. The reason is that many measures that are intended to improve the living environment led to an increase in the (rental) price and thus reduce the availability of homes.
2. Substantial share of co-creation
Boyd Cohen distinguishes three types of smart city projects. The first type (smart city 1.0) is technology- or corporate-driven. In this case, companies deliver instruments or software 'off the shelf'. For example, the provision of a residential area with adaptive street lighting. The second type (smart city 2.0) is technology enabled, also known as government-driven. In this case, a municipality develops a plan and then issues a tender. For example, connecting and programming traffic light installations, so that emergency services and public transport always receive the green light. The third type (smart city 3.0) is community-driven and based on citizen co-creation, for example an energy cooperative. In the latter case, there is the greatest chance that the wishes of the citizens concerned will come first.
A good example of co-creation between different stakeholders is the development of the Brain port Smart District in Helmond, a mixed neighborhood where living, working, generating energy, producing food, and regulating a circular neighborhood will go hand in hand. The future residents and entrepreneurs, together with experts, are investigating which state-of-the-art technology can help them with this.
Bias among developers plays a major role in the use of artificial intelligence. The best way to combat bias (and for a variety of other reasons, too) is to use diversity as a criterion when building development teams. But also (ethical) committees that monitor the responsible purchasing and use of (digital) technologies are better equipped for their task the more diverse they are.
Respecting urban complexity
In his essay The porous city, Gavin Starks describes how smart cities, with their technical utopianism and marketing jargon, ignore the plurality of the drivers of human behavior and instead see people primarily as homo economicus, driven by material gain and self-interest.
The best example is Singapore – the number 1 on the Smart City list, where techno-utopianism reigns supreme. This one-party state provides prosperity, convenience, and luxury using the most diverse digital aids to everyone who exhibits desirable behavior. There is little room for a differing opinion. A rapidly growing number of CCTV cameras – soon to be 200,000 – ensures that everyone literally stays within the lines. If not, the culprit can be quickly located with automatic facial recognition and crowd analytics.
Anyone who wants to understand human life in the city and does not want to start from simplistic assumptions such as homo economicus must respect the complexity of the city, try to understand it, and know that careless intervention might have huge unintended consequences.
The complexity of the city is the main argument against the use of reductionist adjectives such as 'smart', but also 'sharing', circular, climate-neutral', ‘resilient' and more. In addition, the term smart refers to a means that is rarely seen as an aim as such. If an adjective were desirable, I prefer the term 'humane city'.
But whatever you name a city, it is necessary to emphasize that it is a complex organism with many facets, the coherence of which must be well understood by all stakeholders for the city to prosper and its inhabitants to be happy.
Digitization. Two tracks
City authorities that are aware of the complexity of their city can best approach digitization along two tracks. The first aims to translate the city's problems and ambitions into policy and consider digital instruments a part of the whole array of other instruments. The second track is the application of ethical principles in the search for and development of digital tools. Both tracks influence each other.
Track 1: The contribution of digital technology
Digital technology is no more or less than one of the instruments with which a city works towards an ecologically and socially sustainable future. To articulate what such a future is meaning, I introduced Kate Raworth's ideas about the donut economy (episode 9). Designing a vision for the future must be a broadly supported democratic process. In this process, citizens also check the solution of their own problems against the prosperity of future generations and of people elsewhere in the world. Furthermore, policy makers must seamlessly integrate digital and other policy instruments, such as legislation, funding, and information provision (episode 8).
The most important question when it comes to (digital) technology is therefore which (digital) technological tools contribute to the realization of a socially and ecologically sustainable city.
Track 2: The ethical use of technology
In the world in which we realize the sustainable city of the future, digital technology is developing rapidly. Cities are confronted with these technologies through powerful smart city technology marketing. The most important question that cities should ask themselves in this regard is How do we evaluate the technology offered and that we want to develop from an ethical perspective. The first to be confronted with this question—besides hopefully the industry itself—is the department of the Chief Information or Technology officer. He or she naturally participates in the first track-process and can advise policymakers at an early stage. I previously inventoried (ethical) criteria that play a role in the assessment of technological instrument.
In the management of cities, both tracks come together, resulting in one central question: Which (digital) technologies are eligible to support us towards a sustainable future in a responsible way. This series has not provided a ready-made answer; this depends on the policy content and context. However, the successive editions of this series will have provided necessary constituents of the answer.
In my e-book Cities of the Future. Always humane, smart if helpful, I have carried out the policy process as described above, based on current knowledge about urban policy and urban developments. This has led to the identification of 13 themes and 75 actions, with references to potentially useful technology. You can download the e-book here:
Mateusz Jarosiewicz And His Smart City Projects For A Smart Future - UAE TIMES, A Gulf Newspaper, Dubai News, Gulf News, Abu Dhabi News, UAE News
Energy, food, mobility, finance – just about every global sector is expected to transform dramatically in the coming decades. So isn’t it time you create your own transition strategy?
Do you believe, like us, that sustainability and social justice are key to fundamental change? Then join ‘Just Sustainability Transitions’: a hands-on, six-month course that provides the tools and inspiration needed to facilitate change processes.
Is your company looking for a framework to accelerate and manage its positive impact on people and the planet? Join the City of Amsterdam's Building Better Business meetups and programme to pursue a B Corp or Economy for the Common Good certification!
Building Better Business (BBB) has two different tracks: B Corp and Economy for the Common Good (ECG). You can join either track to transform your business into a change agent and build the foundation for certification – if you decide to take that step.
During our online meetups, you will have the opportunity to learn more about the BBB programme and find out which path to certification works best for your company!
Please note: the 1 April meetup focuses exclusively on the ECG certification model, so do join that edition if you would like to dig deeper. You can sign up via the same Eventbrite link
Diversity & Inclusion has become a key objective for successful businesses of today. Companies are facing multiple challenges implementing diversity strategies in their daily operations and culture. Where can I find tech talent from diverse backgrounds? Who is working on making the local talent pool more diverse? How can I find a partner that meets my needs?
TOMAS Connect #1 is an interactive matchmaking between companies and a number of talent development initiatives in the Amsterdam region that stimulate diversity and inclusion in tech.
In just 1,5 hours, you will meet the key educators, initiatives and programs that have a proven track record in contributing to the tech sector and could therefore help you diversify your hiring pipeline. Take the opportunity to learn what’s out there, how they can help you and what you can do to support them.
* Ecosystem overview: who is working on diversity and inclusion in talent development?
* Pitches: meet 8 different initiatives
* Breakout areas: connect & ask questions
Is your company looking for a framework to accelerate and manage its impact on people and the planet?
Join the City of Amsterdam's Building Better Business (BBB) programme to explore how you can be part of a more sustainable and inclusive economy, and pursue a B Corp or Economy for the Common Good (ECG) certification! And sign up for this free event to hear from new economy leaders, connect with other impact-minded companies, and learn the ins and outs of the BBB tracks.
BBB event speakers
The BBB event features a keynote by Michael Weatherhead, New Opportunities and Finance Lead of Wellbeing Economy Alliance and contributions from:
- Katie Hill (B Lab Europe),
- Robin Foolen (B Corp-certified company Secrid),
- Christian Felber (initiator of Economy of the Common Good),
- Joost Broeders (ECG-certified company Baril Coatings).
Who is the BBB event for?
The BBB programme and its inspiration event are geared towards Amsterdam Metropolitan Area-based companies that want to formalise their social impact ambitions and make the transition to a sustainable business model.
Op 22 maart organiseert The Present een event voor iedereen die wil ontdekken hoe zijn of haar kwaliteiten een bijdrage kunnen leveren aan een sociale en inclusieve samenleving. Tijdens deze middag vertelt Kees Klomp over het belang van de betekeniseconomie, delen ondernemers hun ervaringen en laten verscheidene bijzondere initiatieven zien hoe zij een bijdrage leveren aan het leven van anderen. Ontmoet gelijkgestemden en raak geïnspireerd door concrete voorbeelden van andere ondernemers. Ben jij klaar om te ondernemen voor de toekomst? Kom langs op 22 maart!
“Ondernemers staan voor visie, verandering en daadkracht. Precies de drie dingen die nodig zijn voor een duurzame en sociale toekomst.”
Over The Present
The Present (www.thepresent.world) is een ondernemersplatform met een frisse blik op ondernemerschap. Door middel van campagnes, events en actieve matchmaking helpt The Present ondernemers zich bewust(er) te worden van de positieve rol die zij kunnen spelen in de samenleving.
The 16th episode of the series Building sustainable cities - The contribution of digital technology reveals what can happen if the power of artificial intelligence is not used in a responsible manner.
The fight against crime in the United States, has been the scene of artificial intelligence’s abuse for years. As will become apparent, this is not only the result of bias. In episode 11, I discussed why artificial intelligence is a fundamentally new way of using computers. Until then, computers were programmed to perform operations such as structuring data and making decisions. In the case of artificial intelligence, they are trained to do so. However, it is still people who design the instructions (algorithms) and are responsible for the outcomes, although the way in which the computer performs its calculations is increasingly becoming a 'black box'.
Applications of artificial intelligence in the police
Experienced detectives are traditionally trained to compare the 'modus operandi' of crimes to track down perpetrators. Due to the labor-intensive nature of the manual implementation, the question soon arose as to whether computers could be of assistance. A first attempt to do so in 2012 in collaboration with the Massachusetts Institute of Technology resulted in grouping past crimes into clusters that were likely to have been committed by the same perpetrator(s). When creating the algorithm, the intuition of experienced police officers was the starting point. Sometimes it was possible to predict where and when a burglar might strike, leading to additional surveillance and an arrest.
These first attempts were soon refined and taken up by commercial companies. 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 it has been calculated that the risk of crimes (vandalism, burglary, violence) is then greatest. Anyone who behaves 'suspiciously' risks to be arrested. Facial recognition plays also an important role in this.
Both predictive policing and facial recognition are based on a "learning set" of tens 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. This composition has everything to do with the working method of the police. Although drug use in cities in the US is common in all neighborhoods, policing based on PredPol and similar systems is focused on a few neighborhoods (of color). Then, it is not surprising that most drug-related crimes are retrieved there and, as a result, the composition of the database became even more skewed.
In these cases, 'bias' is the cause of the unethical effect of the application of artificial intelligence. Algorithms always reflect the assumptions, views, and values of their creators. They do not predict the future, but make sure that the past is reproduced. This also applies to applications outside the police force. The St. George Hospital Medical School in London has employed disproportionately many white males for at least a decade because the leather set reflected the incumbent staff. The criticized Dutch System Risk Indication System also uses historical data about fines, debts, benefits, education, and integration to search more effectively for people who abuse benefits or allowances. This is not objectionable but should never lead to 'automatic' incrimination without further investigation and the exclusion of less obvious persons.
The simple fact that the police have a disproportionate presence in alleged hotspots and are very keen on any form of suspicious behavior means that the number of confrontations with violent results has increased rapidly. In 2017 alone, police crackdowns in the US resulted in an unprecedented 1,100 casualties, of which only a limited number of whites. In addition, the police have been engaged in racial profiling for decades. Between 2004-2012, the New York Police Department checked more than 4.4 million residents. Most of these checks resulted in no further action. In about 83% of the cases, the person was black or Latino, although the two groups together make up just over half of the population. For many citizens of colour in the US, the police do not represent 'the good', but have become part of a hostile state power.
In New York, in 2017, a municipal provision to regulate the use of artificial intelligence was proposed, the Public Oversight of Surveillance Technology Act (POST). The Legal Defense and Educational Fund, a prominent US civil rights organization, urged the New York City Council to ban the use of data made available because of discriminatory or biased enforcement policies. This wish was granted in June 2019, and this resulted in the number of persons included in the database being reduced from 42,000 to 18,000. It concerned all persons who had been included in the system without concrete suspicion.
San Francisco, Portland, and a range of other cities have gone a few steps further and banned the use of facial recognition technology by police and other public authorities. Experts recognize that the artificial intelligence underlying facial recognition systems is still imprecise, especially when it comes to identifying the non-white population.
The societal roots of crime
Knowledge of how to reduce bias in algorithms has grown, but instead of solving the problem, awareness has grown into a much deeper problem. It is about the causes of crime itself and the realization that the police can never remove them.
Crime and recidivism are associated with inequality, poverty, poor housing, unemployment, use of alcohol and drugs, and untreated mental illness. These are also dominant characteristics of neighborhoods with a lot of crime. As a result, residents of these neighborhoods are unable to lead a decent life. These conditions are stressors that influence the quality of the parent-child relationship too: attachment problems, insufficient parental supervision, including tolerance of alcohol and drugs, lack of discipline or an excess of authoritarian behavior. All in all, these conditions increase the likelihood that young people will be involved in crime, and they diminish the prospect of a successful career in school and elsewhere.
The ultimate measures to reduce crime in the longer term and to improve security are: sufficient income, adequate housing, affordable childcare, especially for 'broken families' and unwed mothers and ample opportunities for girls' education. But also, care for young people who have encountered crime for the first time, to prevent them from making the mistake again.
This will not solve the problems in the short term. A large proportion of those arrested by the police in the US are addicted to drugs or alcohol, are severely mentally disturbed, have serious problems in their home environment - if any - and have given up hope for a better future. Based on this understanding, the police in Johnson County, Kansas, have been calling for help from mental health professionals for years, rather than handcuffing those arrested right away. This approach has proved successful and caught the attention of the White House during the Obama administration. Lynn Overmann, who works as a senior advisor in the president’s technology office, has therefore started the Data-Driven Justice Initiative. The immediate reason was that the prisons appeared to be crowded by seriously disturbed psychiatric patients. Coincidentally, Johnson County had an integrated data system that stores both crime and health data. In other cities, these are kept in incomparable data silos. Together with the University of Chicago Data Science for Social Good Program, artificial intelligence was used 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.
What does this example teach? The rise of artificial intelligence caused Wire editor Chris Anderson to call it the end of the theory. He couldn't be more wrong! Theory has never disappeared; at most it has disappeared from the consciousness of those who work with artificial intelligence. In his book The end of policing, Alex Vitale concludes: Unless cities alter the police's core functions and values, use by police of even the most fair and accurate algorithms is likely to enhance discriminatory and unjust outcomes (p. 28). Ben Green adds: The assumption is: we predicted crime here and you send in police. But what if you used data and sent in resources? (The smart enough city, p. 78).
The point is to replace the dominant paradigm of identifying, prosecuting and incarcerating criminals with the paradigm of finding potential offenders in a timely manner and giving them the help, they need. It turns out that it's even cheaper. The need for the use of artificial intelligence is not diminishing, but the training of the computers, including the composition of the training sets, must change significantly. It is therefore recommended that diverse and independent teams design such a training program based on a scientifically based view of the underlying problem and not leaving it to the police itself.
This article is a condensed version of an earlier article The Safe City (September 2019), which you can read by following the link below, supplemented with data from Chapter 4 Machine learning's social and political foundationsfrom Ben Green's book The smart enough city (2020).
In the first part of the series, I explained why digital technology 'for the good' is a challenge. The second part dealt with ethical criteria behind its responsible use. In the third part I have selected important field that will benefit from the responsible application of digital technology:
16 Abuse of artificial intelligence by the police in the US. More than bias
17 How can digital tools help residents to regain ownership of the city?
18 Will MaaS reduce the use of cars?
19 Digital tools as enablers towards a circular economy
20 Smart grids: where social and digital innovation meet
21 Risks and opportunities of digitization in healthcare
22 Two 100-city missions: Ill-considered leaps forward
23 Epilogue: Beyond the smart city
The link below enables you to open all previous episodes, also in Dutch language.
Ben jij geïnteresseerd in de laatste trends in food, mode, circulair design, en duurzaamheid? Houd je van een internationale werkomgeving en werk je graag met bekende merken? En wil je je verdiepen in de laatste digital marketing ontwikkelingen & tools op onze kosten? Lees dan door.
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In the 12th and 13th episode of the series Better cities: The contribution of digital technology, I will use the ethical principles from the 9th episode to assess several applications of digital technology. This episode discusses: (1) Internet of Things, (2) robotics and (3) biometrics. Next week I will continue with (4) Immersive technology (augmented and virtual reality), (5) blockchain and (6) platforms.
These techniques establish reciprocal connections (cybernetic loops) between the physical and the digital world. I will describe each of them briefly, followed by comments on their ethical aspects: privacy, autonomy, security, control, human dignity, justice, and power relations, insofar relevant. The book Opwaarderen: Borgen van publieke waarden in de digitale samenleving. Rathenau Instituut 2017 proved to be valuable for this purpose. Rathenau Institute 2017.
The Internet-of-Things connects objects via sensors with devices that process this data (remotely). The pedometer on the smartphone is an example of data collection on people. In time, data about everyone's health might be collected and evaluated at distance. For the time being, this mainly concerns data of objects. A well-known example is the 'smart meter'. More and more household equipment is connected to the Internet and transmits data about their use. For a long time, Samsung smart televisions had a built-in television camera and microphone with which the behavior of the viewers could be observed. Digital roommates such as Alexa and Siri are also technically able to pass on everything that is said in their environment to their bosses.
Machines, but also trains and trucks are full of sensors to monitor their functioning. Traffic is tracked with sensors of all kinds which measure among many others the quantity of exhaust gases and particulate matter. In many places in the world, people are be monitored with hundreds of thousands CCTV’s. A simple signature from American owners of a Ring doorbell is enough to pass on to the police the countenance of those who come to the front door. Orwell couldn't have imagined.
Internet of Things makes it possible to always track every person, inside and outside the home. When it comes to collecting data in-house, the biggest problem is obscurity and lack of transparency. Digital home-law can be a solution, meaning that no device collects data unless explicit permission is given. A better solution is for manufacturers to think about why they want to collect all this data at all.
Once someone leaves the house, things get trickier. In many Dutch cities 'tracking' of mobile phones has been banned, but elsewhere a range of means is available to register everyone's (purchasing) behavior. Fortunately, legislation on this point in Europe is becoming increasingly strict.
The goal of constant addition of more 'gadges' to devices and selling them as 'smart' is to entice people to buy them, even if previous versions are far from worn out. Sailing is surrounded by all of persuasive techniques that affect people's free will. Facebook very deftly influences our moods through the selection of its newsfeeds. Media, advertisers, and companies should consider the desirability of taking a few steps back in this regard. For the sake of people and the environment.
Sensors in home appliances use to be poorly secured and give cybercriminals easy access to other devices. For those who want to control their devices centrally and want them to communicate with each other’s too, a closed network - a form of 'edge computing' - is a solution. Owners can then decide for themselves which data may be 'exposed', for example for alarms or for balancing the electricity network. I will come back to that in a later episode.
People who, for example, control the lighting of their home via an app, are already experiencing problems when the phone battery is empty. Experience also shows that setting up a wireless system is not easy and that unwanted interferences often occur. Simply changing a lamp is no longer sufficient to solve this kinds of problems. For many people, control over their own home slips out of their hands.
The digital component of many devices and in particular the dependence on well-configured software makes people increasingly dependent on suppliers, who at the same time are less and less able to meet the associated demand for service and support.
Robotics is making its appearance at great speed. In almost every heart surgery, robotics is used to make the surgeon's movements more precise, and some operations are performed (almost) completely automatically. Robots are increasingly being used in healthcare, to support or replace healthcare providers. Also think of robots that can observe 3D and crawl through the sewage system. They help to solve or prevent leakages, or they take samples to detect sources of contamination. Leeds aims to be the first 'self-repairing city' by 2035. ‘Self-driving' cars and metro trains are other examples. Most warehouses and factories are full of robots. They are also making their appearance in households, such as vacuum cleaners or lawnmowers. Robots transmit large amounts of information and are therefore essential parts of the Internet of Things.
Robots are often at odds with privacy 'by design'. This applies definitely to robots in healthcare. Still, such devices are valuable if patients and/or their relatives are sufficiently aware of their impact. Transparency is essential as well as trust that these devices only collect and transmit data for the purpose for which they are intended.
Many people find 'reversing parking' a problem and prefer to leave that to robotics. They thereby give up part of their autonomous driver skills, as the ability to park in reverse is required in various other situations. This is even more true for skills that 'self-driving' cars take over from people. Drivers will increasingly find themselves in situations where they are powerless.
At the same time, robotics is a solution in situations in which people abuse their right to self-determination, for example by speeding, the biggest causes of (fatal) accidents. A mandatory speed limiter saves untold suffering, but the 'king of the road' will not cheer for it.
Leaving operations to robots presupposes that safety is guaranteed. This will not be a problem with robotic lawnmowers, but it is with 'self-driving cars'. Added to this is the risk of hacking into software-driven devices.
Robots can take over boring, 'mind-numbing' dangerous and dirty work, but also work that requires a high degree of precision. Think of manufacturing of computer chips. The biggest problems lie in the potential for job takeovers, which not only has implications for employment, but can also seriously affect quality. In healthcare, people can start to feel 'reified' due to the loss of human contact. For many, daily contact with a care worker is an important instrument against loneliness.
Biometrics encompasses all techniques to identify people by body characteristics: iris, fingerprint, voice, heart rhythm, writing style and emotion. Much is expected of their combination, which is already applied in the passport. There is no escaping security in this world, so biometrics can be a good means of combating identity fraud, especially if different body characteristics are used.
In the US, the application of facial recognition is growing rapidly. In airports, people can often choose to open the security gate 'automatically’ or to stand in line for security. Incode, a San Francisco startup, reports that its digital identity recognition equipment has already been used in 140 million cases by 2021, four times as many as in all previous years combined.
In the EU, the privacy of residents is well regulated by law. The use of data is also laid down in law. Nevertheless, everyone's personal data is stored in countless places.
Facial recognition is provoking a lot of resistance and is increasingly being banned in the public space in the US. This applies to the Netherlands as well.
Biometric technology can also protect privacy by minimization of the information: collected. For example, someone can gain access based on an iris scan, while the computer only checks whether the person concerned has authorization, without registering name.
Cyber criminals are becoming more and more adept at getting hold of personal information. Smaller organizations and sports clubs are especially targeted because of their poor security. If it is also possible to obtain documents such as an identity card, then identity fraud is lurking.
Combining different identification techniques as happens in passports, contributes to the rightful establishing someone's identity. This also makes counterfeiting of identity documents more difficult. Other less secured documents, for example driver's licenses and debit cards, can still be counterfeited or (temporarily) used after they have been stolen, making identity theft relatively easy.
The opposition to facial recognition isn't just about its obvious flaws; the technology will undoubtedly improve in the coming years. Much of the danger lies in the underlying software, in which bias is difficult to eliminate.
When it comes to human dignity, there is also a positive side to biometrics. Worldwide, billions of people are unable to prove who they are. India's Aadhar program is estimated to have provided an accepted form of digital identity based on biometrics to 1.1 billion people. The effect is that financial inclusion of women has increased significantly.
In many situations where biometric identification has been applied, the problem of reversed burden of proof arises. If there is a mistaken identity, the victim must prove that he is not the person the police suspect is.
To be continued next week.
The link below opens an overview of all published and future articles in this series. https://www.dropbox.com/s/vnp7b75c1segi4h/Voorlopig%20overzicht%20van%20materialen.docx?dl=0
Applications for the 2022 New European Bauhaus prizes are open. Following the success of the first prizes that received more than 2,000 applications last year, the 2022 edition will celebrate new inspiring examples of the transformations the initiative wants to bring about in our daily lives, living spaces and experiences. As in the first edition, the New European Bauhaus prizes 2022 will award young talents' ideas as well as existing projects for sustainability, inclusiveness and aesthetics bringing the European Green deal to people and local communities.
Prizes will be awarded to projects and ideas that contribute to beautiful, sustainable and inclusive places, in four categories:
- Reconnecting with nature;
- Regaining a sense of belonging;
- Prioritising the places and people that need it most;
- Fostering long-term, lifecycle and integrated thinking in the industrial ecosystem.
Applications are open until 28 February 2022 at 19:00 CET.
For more information and to submit your application visit: https://prizes.new-european-bauhaus.eu/
In the 11th episode of the series Better cities: The contribution of digital technology, I will apply the ethical principles from episode 9 to the design and use of artificial intelligence.
Before, I will briefly summarize the main features of artificial intelligence, such as big data, algorithms, deep-learning, and machine learning. For those who want to know more: Radical technologies by Adam Greenfield (2017) is a very readable introduction, also regarding technologies such as blockchain, augmented and virtual reality, Internet of Things, and robotics, which will be discussed in next episodes.
Artificial intelligence has valuable applications but also gross forms of abuse. Valuable, for example, is the use of artificial intelligence in the layout of houses and neighborhoods, taking into account ease of use, views and sunlight with AI technology from Spacemaker or measuring the noise in the center of Genk using Nokia's Scene Analytics technology. It is reprehensible how the police in the US discriminate against population groups with programs such as PredPol and how the Dutch government has dealt in the so called ‘toelagenaffaire’.
Thanks to artificial intelligence, a computer can independently recognize patterns. Recognizing patterns as such is nothing new. This has long been possible with computer programs written for that purpose. For example, to distinguish images of dogs and cats, a programmer created an "if....then" description of all relevant characteristics of dogs and cats that enabled a computer to distinguish between pictures of the two animal species. The number of errors depended on the level of detail of the program. When it comes to more types of animals and animals that have been photographed from different angles, making such a program is very complicated. In that case, a computer can be trained to distinguish relevant patterns itself. In this case we speak of artificial intelligence. People still play 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 a large number of examples, for example of animals that are labeled as dog or cat and if necessary lion tiger and more . The computer then searches 'itself' for associated characteristics. If there are still too many errors, new images will be added.
The way in which the animals are depicted can vary endlessly, whereby it is no longer about their characteristics, but about shadow effect, movement, position of the camera or the nature of the movement, in the case of moving images. The biggest challenge is to teach the computer to take these contextual characteristics into account as well. This is done through the imitation of the neural networks. Image recognition takes place just like in our brains thanks to distinguishing layers, varying from distinguishing simple lines, patterns, and colors to differences in sharpness. Because of this layering, we speak of 'deep learning'. This obviously involves large data sets and a lot of computing power, but it is also a labor-intensive process.
Learning how to apply algorithms under supervision produces reliable results and the instructor can still explain the result after many iterations. As the situation becomes more complicated and different processes are proceeding at the same time, guided instruction is not feasible any longer. For example, if animals attack each other, surviving or not, and the computer must predict which kind of animals have the best chance of survival under which conditions. Also think of the patterns that the computer of a car must be able to distinguish to be able to drive safely on of the almost unlimited variation, supervised learning no longer works.
In the case of unsupervised learning, the computer is fed with data from many millions of realistic situations, in the case of cars recordings of traffic situations and the way the drivers reacted to them. Here we can rightly speak of 'big data' and 'machine learning', although these terms are often used more broadly. For example, the car's computer 'learns' how and when it must stay within the lanes, can pass, how pedestrians, bicycles or other 'objects' can be avoided, what traffic signs mean and what the corresponding action is. Tesla’s still pass all this data on to a data center, which distills patterns from it that regularly update the 'autopilots' of the whole fleet. In the long run, every Tesla, anywhere in the world, should recognize every imaginable pattern, respond correctly and thus guarantee the highest possible level of safety. This is apparently not the case yet and Tesla's 'autopilot' may therefore not be used without the presence of a driver 'in control'. Nobody knows by what criteria a Tesla's algorithms work.
Unsupervised learning is also applied when it comes to the prediction of (tax) fraud, the chance that certain people will 'make a mistake' or in which places the risk of a crime is greatest at a certain moment. But also, in the assessment of applicants and the allocation of housing. For all these purposes, the value of artificial intelligence is overestimated. Here too, the 'decisions' that a computer make are a 'black box'. Partly for this reason, it is difficult, if not impossible, to trace and correct any errors afterwards. This is one of the problems with the infamous ‘toelagenaffaire’.
The cybernetic loop
Algorithmic decision-making is part of a new digital wave, characterized by a 'cybernetic loop' of measuring (collecting data), profiling (analyzing data) and intervening (applying data). These aspects are also reflected in every decision-making process, but the parties involved, politicians and representatives of the people make conscious choices step by step, while the entire process is now partly a black box.
The role of ethical principles
Meanwhile, concerns are growing about ignoring ethical principles using artificial intelligence. This applies to near all principles that are discussed in the 9th episode: violation of privacy, discrimination, lack of transparency and abuse of power resulting in great (partly unintentional) suffering, risks to the security of critical infrastructure, the erosion of human intelligence and undermining of trust in society. It is therefore necessary to formulate guidelines that align the application of artificial intelligence again with these ethical principles.
An interesting impetus to this end is given in the publication of the Institute of Electric and Electronic Engineers: Ethically Aligned Design: A Vision for Prioritizing Human Well-being with Autonomous and Intelligent Systems. The Rathenau Institute has also published several guidelines in various publications.
The main guidelines that can be distilled from these and other publications are:
1. Placing responsibility for the impact of the use of artificial intelligence on both those who make decisions about its application (political, organizational, or corporate leadership) and the developers. This responsibility concerns the systems used as well as the quality, accuracy, completeness, and representativeness of the data.
2. Prevent designers from (unknowingly) using their own standards when instructing learning processes. Teams with a diversity of backgrounds are a good way to prevent this.
3. To be able to trace back 'decisions' by computer systems to the algorithms used, to understand their operation and to be able to explain them.
4. To be able to scientifically substantiate the model that underlies the algorithm and the choice of data.
5. Manually verifying 'decisions' that have a negative impact on the data subject.
6. Excluding all forms of bias in the content of datasets, the application of algorithms and the handling of outcomes.
7. Accountability for the legal basis of the combination of datasets.
8. Determine whether the calculation aims to minimize false positives or false negatives.
9. Personal feedback to clients in case of lack of clarity in computerized ‘decisions’.
10. Applying the principles of proportionality and subsidiarity, which means determining on a case-by-case basis whether the benefits of using artificial intelligence outweigh the risks.
11. Prohibiting applications of artificial intelligence that pose a high risk of violating ethical principles, such as facial recognition, persuasive techniques and deep-fake techniques.
12. Revocation of legal provisions if it appears that they cannot be enforced in a transparent manner due to their complexity or vagueness.
The third, fourth and fifth directives must be seen in conjunction. I explain why below.
The scientific by-pass of algorithmic decision making
When using machine learning, computers themselves adapt and extend the algorithms and combine data from different data sets. As a result, the final ‘decisions’ made by the computer cannot be explained. This is only acceptable after it has been proven that these decisions are 'flawless', for example because, in the case of 'self-driving' cars, if they turn out to be many times safer than ordinary cars, which - by the way - is not the case yet.
Unfortunately, this was not the case too in the ‘toelagenaffaire’. The fourth guideline could have provided a solution. Scientific design-oriented research can be used to reconstruct the steps of a decision-making process to determine who is entitled to receive an allowance. By applying this decision tree to a sufficiently large sample of cases, the (degree of) correctness of the computer's 'decisions' can be verified. If this is indeed the case, then the criteria used in the manual calculation may be used to explain the processes in the computer's 'black box'. If there are too many deviations, then the computer calculation must be rejected at all.
In the US, the use of algorithms in the public sector has come in a bad light, especially because of the facial recognition practices that will be discussed in the next episode. The city of New York has therefore appointed an algorithm manager, who investigates whether the algorithms used comply with ethical and legal rules. KPMG has a supervisory role in Amsterdam. In other municipalities, we see that role more and more often fulfilled by an ethics committee.
In the European public domain, steps have already been taken to combat excesses of algorithmic decision-making. The General Data Protection Regulation (GDPR), which came into effect in 2018, has significantly improved privacy protection. In April 2019, the European High Level Expert Group on AI published ethical guidelines for the application of artificial intelligence. In February 2020, the European Commission also established such guidelines, including in the White Paper on Artificial Intelligence and an AI regulation. The government also adopted the national digitization strategy, the Strategic Action Plan for AI and the policy letter on AI, human rights, and public values.
I realize that binding governments and their executive bodies to ethical principles is grist to the mill for those who flout those principles. Therefore, the search for the legitimate use of artificial intelligence to detect crime, violations or abuse of subsidies and many other applications continues to deserve broad support.
Follow the link below to find one of the previous episodes or see which episodes are next, and this one for the Dutch version.