AI And Open Data Show Just How Often Cars Block Bus And Bike Lanes

Curated on Posted on by Stefaan Verhulst

Eillie Anzilotti in Fast Company: “…While anyone who bikes or rides a bus in New York City knows intuitively that the lanes are often blocked, there’s been little data to back up that feeling apart from the fact that last year, the NYPD issues 24,000 tickets for vehicles blocking bus lanes, and around 79,000 to cars in the bike lane. By building the algorithm, Bell essentializes what engaged citizenship and productive use of open data looks like. The New York City Department of Transportation maintains several hundred video cameras throughout the city; those cameras feed images in real time to the DOT’s open-data portal. Bell downloaded a week’s worth of footage from that portal to analyze.

To build his computer algorithm to do the analysis, he fed around 2,000 images of buses, cars, pedestrians, and vehicles like UPS trucks into TensorFlow, Google’s open-source framework that the tech giant is using to train autonomous vehicles to recognize other road users. “Because of the push into AVs, machine learning in general and neural networks have made lots of progress, because they have to answer the same questions of: What is this vehicle, and what is it going to do?” Bell says. After several rounds of processing, Bell was able to come up with an algorithm that fairly faultlessly could determine if a vehicle at the bus stop was, in fact, a bus, or if it was something else that wasn’t supposed to be there.

As cities and governments, spurred by organizations like OpenGov, have moved to embrace transparency and open data, the question remains: So, what do you do with it?

For Bell, the answer is that citizens can use it to empower themselves. “I’m a little uncomfortable with cameras and surveillance in cities,” Bell says. “But agencies like the NYPD and DOT have already made the decision to put the cameras up. We don’t know the positive and negative outcomes if more and more data from cameras is opened to the public, but if the cameras are going in, we should know what data they’re collecting and be able to access it,” he says. He’s made his algorithm publicly available in the hopes that more people will use data to investigate the issue on their own streets, and perhaps in other cities….Bell is optimistic that open data can empower more citizens to identify issues in their own cities and bring a case for why they need to be addressed….(More)”.

Can Data Help Brazil Take a Bite Out of Crime?

Curated on Posted on by Stefaan Verhulst

Joe Leahy at ZY See Beyond: “When Argentine entrepreneur Federico Vega two years ago launched a startup offering Uberlike services for Brazil’s freight industry, the sector was on the cusp of a wave of cargo theft.

Across Brazil, but especially in Rio de Janeiro, crime has soared, with armed gangs robbing one truck every 50 minutes in Rio last year.

But while the authorities have reacted with force to the crime wave, Vega turned to software engineers at his CargoX startup. By studying a range of industry and security data, CargoX developed software that identifies risks and helps drivers avoid crime hot spots, or if a robbery does happen, alerts the company in real time.CargoX says that in Brazil, 0.1 percent by value of all cargo transported by trucks is stolen. “We are about 50 percent lower than that, but we still have tons of work to do,” says São Paulo–based Vega.

CargoX is one of a growing number of Brazilian technology startups that are seeking digital solutions to the problem of endemic crime in Latin America’s largest country.

Having started from zero two years ago, CargoX today has signed up more than 5,000 truckers. The company scans data from all sources to screen its motorists and study past crimes to see what routes, times, neighborhoods and types of cargo represent the highest risk.

Certain gas stations that might, for instance, be known for prostitution are avoided because of their criminal associations. Daytime delivery is better than night. Drivers are tracked by GPS and must stay inside “geofences” — known safe routes. Foraying outside these alerts the system.

Vega says the key is to learn from the data. “Everyone says it’s good to learn from your mistakes, but it’s even better to learn from other people’s mistakes.”

The use of big data to anticipate crime is at the center of the approach of another tech-savvy entrepreneur, Pedro Moura Costa, the founder of BVRio Institute, an organization that seeks market solutions to environmental issues.

Organized crime is targeting everything from highway robbery to the illegal plunder of tropical hardwoods in the Amazon while online crime such as credit card fraud is also rampant, analysts say….(More)”.

How the government will operate in 2030

Curated on Posted on by Stefaan Verhulst

Darrell West at the Hill: “Imagine it is 2030 and you are a U.S. government employee working from home. With the assistance of the latest technology, you participate in video calls with clients and colleagues, augment your job activities through artificial intelligence and a personal digital assistant, work through collaboration software, and regularly get rated on a one-to-five scale by clients regarding your helpfulness, follow-through, and task completion.

How did you — and the government — get here? The sharing economy that unfolded in 2018 has revolutionized the public-sector workforce. The days when federal employees were subject to a centrally directed Office of Personnel and Management that oversaw permanent, full-time workers sitting in downtown office buildings are long gone. In their place is a remote workforce staffed by a mix of short- and long-term employees. This has dramatically improved worker productivity and satisfaction.

In the new digital world that has emerged, the goal is to use technology to make employees accountable. Gone are 20- or 30-year careers in the federal bureaucracy. Political leaders have always preached the virtue of running government like a business, and the success of Uber, Airbnb, and WeWork has persuaded them to focus on accountability and performance.

Companies such as Facebook demonstrated they could run large and complex organizations with less than 20,000 employees, and the federal government followed suit in the late 2020s. Now, workers deploy the latest tools of artificial intelligence, virtual reality, data analytics, robots, driverless cars, and digital assistants to improve the government. Unlike the widespread mistrust and cynicism that had poisoned attitudes in the decades before, the general public now sees government as a force for achieving positive results.

Many parts of the federal government are decentralized and mid-level employees are given greater authority to make decisions — but are subject to digital ratings that keep them accountable for their performance. The U.S. government borrowed this technique from China, where airport authorities in 2018 installed digital devices that allowed visitors to rate the performance of individual passport officers after every encounter. The reams of data have enabled Chinese authorities to fire poor performers and make sure foreign visitors see a friendly and competent face at the Beijing International Airport.

Alexa-like devices are given to all federal employees. The devices are used to keep track of leave time, file reimbursement requests, request time off, and complete a range of routine tasks that used to take employees hours. Through voice-activated commands, they navigate these mundane tasks quickly and efficiently. No one can believe the mountains of paperwork required just a decade ago….(More)”.

A Clever Smartphone Attachment Will Show if Water Is Contaminated

Curated on Posted on by Stefaan Verhulst

Victor Tangermann in Futurism: “…astronomers from the University of Leiden in the Netherlands… are developing a simple smartphone attachment that makes it ridiculously, comically easy to measure the quality of water by pointing the tool at it, nothing more.

The tool’s primary purpose isn’t just so that you can whet your whistle in any lake, river, or creek you deem tasty-looking — quick and precise measurements of water pollution can be hugely beneficial for science. This kind of data can steer environmental policies on a national level. Citizens can tell if their drinking water is contaminated. Fishermen are able to determine the quality of their catch, and how pollution could affect local fish populations. Polluted water can even determine human migration patterns by forcing fishermen to move or give up their trade altogether….

There’s a precedent that have researchers hopeful. In 2013, the same team of astronomers and toxicologists developed the iSPEX (Spectropolarimeter for Planetary EXploration) — a smartphone attachment that can measure air pollution. Dutch citizens, along with people in cities from Athens to London, took thousands of measurements of the particulates in the air. The result: a detailed map of dust particles over the Netherlands and beyond.

The technology behind the smartphone attachment actually is a spin-off of sophisticated astronomy technology that can tell if oxygen is present on planets around other stars. This also foregoes the need to take local samples and send them back to the lab — a relatively expensive process that can take a lot longer….(More)”.

Data for Development: What’s next? Concepts, trends and recommendations

Curated on Posted on by Stefaan Verhulst

Report by the WebFoundation: “The exponential growth of data provides powerful new ways for governments and companies to understand and respond to challenges and opportunities. This report, Data for Development: What’s next, investigates how organisations working in international development can leverage the growing quantity and variety of data to improve their investments and projects so that they better meet people’s needs.

Investigating the state of data for development and identifying emerging data trends, the study provides recommendations to support German development cooperation actors seeking to integrate data strategies and investments in their work. These insights can guide any organisation seeking to use data to enhance their development work.

The research considers four types of data: (1) big data, (2) open data, (3) citizen-generated data and (4) real-time data, and examines how they are currently being used in development-related policy-making and how they might lead to better development outcomes….(More)”.

Lessons from Cambridge Analytica: one way to protect your data

Curated on Posted on by Stefaan Verhulst

Julia Apostle in the Financial Times: “The unsettling revelations about how data firm Cambridge Analytica surreptitiously exploited the personal information of Facebook users is yet another demoralising reminder of how much data has been amassed about us, and of how little control we have over it.

Unfortunately, the General Data Protection Regulation privacy laws that are coming into force across Europe — with more demanding consent, transparency and accountability requirements, backed by huge fines — may improve practices, but they will not change the governing paradigm: the law labels those who gather our data as “controllers”. We are merely “subjects”.

But if the past 20 years have taught us anything, it is that when business and legislators have been too slow to adapt to public demand — for goods and services that we did not even know we needed, such as Amazon, Uber and bitcoin — computer scientists have stepped in to fill the void. And so it appears that the realms of data privacy and security are deserving of some disruption. This might come in the form of “self-sovereign identity” systems.

The theory behind self-sovereign identity is that individuals should control the data elements that form the basis of their digital identities, and not centralised authorities such as governments and private companies. In the current online environment, we all have multiple log-ins, usernames, customer IDs and personal data spread across countless platforms and stored in myriad repositories.

Instead of this scattered approach, we should each possess the digital equivalent of a wallet that contains verified pieces of our identities. We can then choose which identification to share, with whom, and when. Self-sovereign identity systems are currently being developed.

They involve the creation of a unique and persistent identifier attributed to an individual (called a decentralised identity), which cannot be taken away. The systems use public/private key cryptography, which enables a user with a private key (a string of numbers) to share information with unlimited recipients who can access the encrypted data if they possess a corresponding public key.

The systems also rely on decentralised ledger applications like blockchain. While key cryptography has been around for a long time, it is the development of decentralised ledger technology, which also supports the trading of cryptocurrencies without the involvement of intermediaries, that will allow self-sovereign identity systems to take off. The potential uses for decentralised identity are legion and small-scale implementation is already happening. The Swiss municipality of Zug started using a decentralised identity system called uPort last year, to allow residents access to certain government services. The municipality announced it will also use the system for voting this spring….

Decentralised identity is more difficult to access and therefore there is less financial incentive for hackers to try. Self-sovereign identity systems could eliminate many of our data privacy concerns while empowering individuals in the online world and turning the established data order on its head. But the success of the technology depends on its widespread adoption….(More)“

Psychographics: the behavioural analysis that helped Cambridge Analytica know voters’ minds

Curated on Posted on by Stefaan Verhulst

Michael Wade at The Conversation: “Much of the discussion has been on how Cambridge Analytica was able to obtain data on more than 50m Facebook users – and how it allegedly failed to delete this data when told to do so. But there is also the matter of what Cambridge Analytica actually did with the data. In fact the data crunching company’s approach represents a step change in how analytics can today be used as a tool to generate insights – and to exert influence.

For example, pollsters have long used segmentation to target particular groups of voters, such as through categorising audiences by gender, age, income, education and family size. Segments can also be created around political affiliation or purchase preferences. The data analytics machine that presidential candidate Hillary Clinton used in her 2016 campaign – named Ada after the 19th-century mathematician and early computing pioneer – used state-of-the-art segmentation techniques to target groups of eligible voters in the same way that Barack Obama had done four years previously.

Cambridge Analytica was contracted to the Trump campaign and provided an entirely new weapon for the election machine. While it also used demographic segments to identify groups of voters, as Clinton’s campaign had, Cambridge Analytica also segmented using psychographics. As definitions of class, education, employment, age and so on, demographics are informational. Psychographics are behavioural – a means to segment by personality.

This makes a lot of sense. It’s obvious that two people with the same demographic profile (for example, white, middle-aged, employed, married men) can have markedly different personalities and opinions. We also know that adapting a message to a person’s personality – whether they are open, introverted, argumentative, and so on – goes a long way to help getting that message across….

There have traditionally been two routes to ascertaining someone’s personality. You can either get to know them really well – usually over an extended time. Or you can get them to take a personality test and ask them to share it with you. Neither of these methods is realistically open to pollsters. Cambridge Analytica found a third way, with the assistance of two University of Cambridge academics.

The first, Aleksandr Kogan, sold them access to 270,000 personality tests completed by Facebook users through an online app he had created for research purposes. Providing the data to Cambridge Analytica was, it seems, against Facebook’s internal code of conduct, but only now in March 2018 has Kogan been banned by Facebook from the platform. In addition, Kogan’s data also came with a bonus: he had reportedly collected Facebook data from the test-takers’ friends – and, at an average of 200 friends per person, that added up to some 50m people.

However, these 50m people had not all taken personality tests. This is where the second Cambridge academic, Michal Kosinski, came in. Kosinski – who is said to believe that micro-targeting based on online data could strengthen democracy – had figured out a way to reverse engineer a personality profile from Facebook activity such as likes. Whether you choose to like pictures of sunsets, puppies or people apparently says a lot about your personality. So much, in fact, that on the basis of 300 likes, Kosinski’s model is able to predict someone’s personality profile with the same accuracy as a spouse….(More)”

Cambridge Analytica scandal: legitimate researchers using Facebook data could be collateral damage

Curated on Posted on by Stefaan Verhulst

 at The Conversation: “The scandal that has erupted around Cambridge Analytica’s alleged harvesting of 50m Facebook profiles assembled from data provided by a UK-based academic and his company is a worrying development for legitimate researchers.

Political data analytics company Cambridge Analytica – which is affiliated with Strategic Communication Laboratories (SCL) – reportedly used Facebook data, after it was handed over by Aleksandr Kogan, a lecturer at the University of Cambridge’s department of psychology.

Kogan, through his company Global Science Research (GSR) – separate from his university work – gleaned the data from a personality test app named “thisisyourdigitallife”. Roughly 270,000 US-based Facebook users voluntarily responded to the test in 2014. But the app also collected data on those participants’ Facebook friends without their consent.

This was possible due to Facebook rules at the time that allowed third-party apps to collect data about a Facebook user’s friends. The Mark Zuckerberg-run company has since changed its policy to prevent such access to developers….

Social media data is a rich source of information for many areas of research in psychology, technology, business and humanities. Some recent examples include using Facebook to predict riots, comparing the use of Facebook with body image concern in adolescent girls and investigating whether Facebook can lower levels of stress responses, with research suggesting that it may enhance and undermine psycho-social constructs related to well-being.

It is right to believe that researchers and their employers value research integrity. But instances where trust has been betrayed by an academic – even if it’s the case that data used for university research purposes wasn’t caught in the crossfire – will have a negative impact on whether participants will continue to trust researchers. It also has implications for research governance and for companies to share data with researchers in the first place.

Universities, research organisations and funders govern the integrity of research with clear and strict ethics proceduresdesigned to protect participants in studies, such as where social media data is used. The harvesting of data without permission from users is considered an unethical activity under commonly understood research standards.

The fallout from the Cambridge Analytica controversy is potentially huge for researchers who rely on social networks for their studies, where data is routinely shared with them for research purposes. Tech companies could become more reluctant to share data with researchers. Facebook is already extremely protective of its data – the worry is that it could become doubly difficult for researchers to legitimately access this information in light of what has happened with Cambridge Analytica….(More)”.

Artificial Intelligence and the Need for Data Fairness in the Global South

Curated on Posted on by Stefaan Verhulst

Medium blog by Yasodara Cordova: “…The data collected by industry represents AI opportunities for governments, to improve their services through innovation. Data-based intelligence promises to increase the efficiency of resource management by improving transparency, logistics, social welfare distribution — and virtually every government service. E-government enthusiasm took of with the realization of the possible applications, such as using AI to fight corruption by automating the fraud-tracking capabilities of cost-control tools. Controversially, the AI enthusiasm has spread to the distribution of social benefits, optimization of tax oversight and control, credit scoring systems, crime prediction systems, and other applications based in personal and sensitive data collection, especially in countries that do not have comprehensive privacy protections.

There are so many potential applications, society may operate very differently in ten years when the “datafixation” has advanced beyond citizen data and into other applications such as energy and natural resource management. However, many countries in the Global South are not being given necessary access to their countries’ own data.

Useful data are everywhere, but only some can take advantage. Beyond smartphones, data can be collected from IoT components in common spaces. Not restricted to urban spaces, data collection includes rural technology like sensors installed in tractors. However, even when the information is related to issues of public importance in developing countries —like data taken from road mesh or vital resources like water and land — it stays hidden under contract rules and public citizens cannot access, and therefore take benefit, from it. This arrangement keeps the public uninformed about their country’s operations. The data collection and distribution frameworks are not built towards healthy partnerships between industry and government preventing countries from realizing the potential outlined in the previous paragraph.

The data necessary to the development of better cities, public policies, and common interest cannot be leveraged if kept in closed silos, yet access often costs more than is justifiable. Data are a primordial resource to all stages of new technology, especially tech adoption and integration, so the necessary long term investment in innovation needs a common ground to start with. The mismatch between the pace of the data collection among big established companies and small, new, and local businesses will likely increase with time, assuming no regulation is introduced for equal access to collected data….

Currently, data independence remains restricted to discussions on the technological infrastructure that supports data extraction. Privacy discussions focus on personal data rather than the digital accumulation of strategic data in closed silos — a necessary discussion not yet addressed. The national interest of data is not being addressed in a framework of economic and social fairness. Access to data, from a policy-making standpoint, needs to find a balance between the extremes of public, open access and limited, commercial use.

A final, but important note: the vast majority of social media act like silos. APIs play an important role in corporate business models, where industry controls the data it collects without reward, let alone user transparency. Negotiation of the specification of APIs to make data a common resource should be considered, for such an effort may align with the citizens’ interest….(More)”.

International Development Doesn’t Care About Patient Privacy

Curated on Posted on by Stefaan Verhulst

Yogesh Rajkotia at the Stanford Social Innovation Review: “In 2013, in southern Mozambique, foreign NGO workers searched for a man whom the local health facility reported as diagnosed with HIV. The workers aimed to verify that the health facility did indeed diagnose and treat him. When they could not find him, they asked the village chief for help. Together with an ever-growing crowd of onlookers, the chief led them to the man’s home. After hesitating and denying, he eventually admitted, in front of the crowd, that he had tested positive and received treatment. With his status made public, he now risked facing stigma, discrimination, and social marginalization. The incident undermined both his health and his ability to live a dignified life.

Similar privacy violations were documented in Burkina Faso in 2016, where community workers asked partners, in the presence of each other, to disclose what individual health services they had obtained.

Why was there such a disregard for the privacy and dignity of these citizens?

As it turns out, unbeknownst to these Mozambican and Burkinabé patients, their local health centers were participating in performance-based financing (PBF) programs financed by foreign assistance agencies. Implemented in more than 35 countries, PBF programs offer health workers financial bonuses for delivering priority health interventions. To ensure that providers do not cheat the system, PBF programs often send verifiers to visit patients’ homes to confirm that they have received specific health services. These verifiers are frequently community members (the World Bank callously notes in its “Performance-Based Financing Toolkit” that even “a local soccer club” can play this role), and this practice, known as “patient tracing,” is common among PBF programs. In World Bank-funded PBF programs alone, 19 out of the 25 PBF programs implement patient tracing. Yet the World Bank’s toolkit never mentions patient privacy or confidentiality. In patient tracing, patients’ rights and dignity are secondary to donor objectives.

Patient tracing within PBF programs is just one example of a bigger problem: Privacy violations are pervasive in global health. Some researchers and policymakers have raised privacy concerns about tuberculosis (TB), human immunodeficiency virus (HIV), family planning, post-abortion care, and disease surveillance programs. A study conducted by the Asia-Pacific Network of People Living with HIV/AIDS found that 34 percent of people living with HIV in India, Indonesia, Philippines, and Thailand reported that health workers breached confidentiality. In many programs, sensitive information about people’s sexual and reproductive health, disease status, and other intimate health details are often collected to improve health system effectiveness and efficiency. Usually, households have no way to opt out, nor any control over how heath care programs use, store, and disseminate this data. At the same time, most programs do not have systems to enforce health workers’ non-disclosure of private information.

In societies with strong stigma around certain health topics—especially sexual and reproductive health—the disclosure of confidential patient information can destroy lives. In contexts where HIV is highly stigmatized, people living with HIV are 2.4 times more likely to delay seeking care until they are seriously ill. In addition to stigma’s harmful effects on people’s health, it can limit individuals’ economic opportunities, cause them to be socially marginalized, and erode their psychological wellbeing….(More)”.