Driven to safety — it’s time to pool our data

Curated on Posted on by Stefaan Verhulst

Kevin Guo at TechCrunch: “…Anyone with experience in the artificial intelligence space will tell you that quality and quantity of training data is one of the most important inputs in building real-world-functional AI. This is why today’s large technology companies continue to collect and keep detailed consumer data, despite recent public backlash. From search engines, to social media, to self driving cars, data — in some cases even more than the underlying technology itself — is what drives value in today’s technology companies.

It should be no surprise then that autonomous vehicle companies do not publicly share data, even in instances of deadly crashes. When it comes to autonomous vehicles, the public interest (making safe self-driving cars available as soon as possible) is clearly at odds with corporate interests (making as much money as possible on the technology).

We need to create industry and regulatory environments in which autonomous vehicle companies compete based upon the quality of their technology — not just upon their ability to spend hundreds of millions of dollars to collect and silo as much data as possible (yes, this is how much gathering this data costs). In today’s environment the inverse is true: autonomous car manufacturers are focusing on are gathering as many miles of data as possible, with the intention of feeding more information into their models than their competitors, all the while avoiding working together….

The complexity of this data is diverse, yet public — I am not suggesting that people hand over private, privileged data, but actively pool and combine what the cars are seeing. There’s a reason that many of the autonomous car companies are driving millions of virtual miles — they’re attempting to get as much active driving data as they can. Beyond the fact that they drove those miles, what truly makes that data something that they have to hoard? By sharing these miles, by seeing as much of the world in as much detail as possible, these companies can focus on making smarter, better autonomous vehicles and bring them to market faster.

If you’re reading this and thinking it’s deeply unfair, I encourage you to once again consider 40,000 people are preventably dying every year in America alone. If you are not compelled by the massive life-saving potential of the technology, consider that publicly licenseable self-driving data sets would accelerate innovation by removing a substantial portion of the capital barrier-to-entry in the space and increasing competition….(More)”

Blockchain systems are tracking food safety and origins

Curated on Posted on by Stefaan Verhulst

Nir Kshetri at The Conversation: “When a Chinese consumer buys a package labeled “Australian beef,” there’s only a 50-50 chance the meat inside is, in fact, Australian beef. It could just as easily contain rat, dog, horse or camel meat – or a mixture of them all. It’s gross and dangerous, but also costly.

Fraud in the global food industry is a multi-billion-dollar problem that has lingered for years, duping consumers and even making them ill. Food manufacturers around the world are concerned – as many as 39 percent of them are worried that their products could be easily counterfeited, and 40 percent say food fraud is hard to detect.

In researching blockchain for more than three years, I have become convinced that this technology’s potential to prevent fraud and strengthen security could fight agricultural fraud and improve food safety. Many companies agree, and are already running various tests, including tracking wine from grape to bottle and even following individual coffee beans through international trade.

Tracing food items

An early trial of a blockchain system to track food from farm to consumer was in 2016, when Walmart collected information about pork being raised in China, where consumers are rightly skeptical about sellers’ claims of what their food is and where it’s from. Employees at a pork farm scanned images of farm inspection reports and livestock health certificates, storing them in a secure online database where the records could not be deleted or modified – only added to.

As the animals moved from farm to slaughter to processing, packaging and then to stores, the drivers of the freight trucks played a key role. At each step, they would collect documents detailing the shipment, storage temperature and other inspections and safety reports, and official stamps as authorities reviewed them – just as they did normally. In Walmart’s test, however, the drivers would photograph those documents and upload them to the blockchain-based database. The company controlled the computers running the database, but government agencies’ systems could also be involved, to further ensure data integrity.

As the pork was packaged for sale, a sticker was put on each container, displaying a smartphone-readable code that would link to that meat’s record on the blockchain. Consumers could scan the code right in the store and assure themselves that they were buying exactly what they thought they were. More recent advances in the technology of the stickers themselves have made them more secure and counterfeit–resistant.

Walmart did similar tests on mangoes imported to the U.S. from Latin America. The company found that it took only 2.2 seconds for consumers to find out an individual fruit’s weight, variety, growing location, time it was harvested, date it passed through U.S. customs, when and where it was sliced, which cold-storage facility the sliced mango was held in and for how long it waited before being delivered to a store….(More)”.

Force Google, Apple and Uber to share mapping data, UK advised

Curated on Posted on by Stefaan Verhulst

Aliya Ram and Madhumita Murgia at the Financial Times: “The UK government should force Google, Apple, Uber and others to share their mapping data so that other companies can develop autonomous cars, drones and transport apps, according to an influential campaign group. The Open Data Institute, co-founded by Tim Berners-Lee at MIT and Nigel Shadbolt, artificial intelligence professor at the University of Oxford, warned on Tuesday that big tech companies had become “data monopolies”.

The group said the UK’s Geospatial Commission should ask the companies to share map data with rivals and the public sector in a collaborative database or else force them to do so with legislation.

“Google along with all of the other companies like Apple and Uber are trying to deliver an excellent service to their clients and customers,” said Jeni Tennison, chief executive of the Open Data Institute. “The status quo is not optimal because all of the organisations we are talking about are replicating effort. This means that people are overall not getting the best service from the data that is being collected and maintained. “The large companies are becoming more like data monopolies and that doesn’t give us the best value from our data.”

On Tuesday, the UK government said its Office for Artificial Intelligence had teamed up with the ODI to pilot two new “data trusts” — legal structures that allow multiple groups to share anonymised information. Data trusts have been described as a good way for small business to compete with large rivals that have lots of data, but only a handful have been set up so far.

The trusts will be designed over the next few months and could be used to share data, for example, about cities, the environment, biodiversity and transport. Ms Tennison said the ODI was also working on a data trust with the mayor of London, Sadiq Khan, and local authorities in Greenwich to see how real time data from the internet of things and sensors could be shared with start-ups to solve problems in the city. London’s transport authority has said ride hailing apps would be forced to turn over travel data to the government. Uber now provides public access to its data on traffic and travel conditions in the UK….(More) (Full Report)”.

Big Data Ethics and Politics: Toward New Understandings

Curated on Posted on by Stefaan Verhulst

Introductory paper by Wenhong Chen and Anabel Quan-Haase of Special Issue of the Social Science Computer Review:  “The hype around big data does not seem to abate nor do the scandals. Privacy breaches in the collection, use, and sharing of big data have affected all the major tech players, be it Facebook, Google, Apple, or Uber, and go beyond the corporate world including governments, municipalities, and educational and health institutions. What has come to light is that enabled by the rapid growth of social media and mobile apps, various stakeholders collect and use large amounts of data, disregarding the ethics and politics.

As big data touch on many realms of daily life and have profound impacts in the social world, the scrutiny around big data practice becomes increasingly relevant. This special issue investigates the ethics and politics of big data using a wide range of theoretical and methodological approaches. Together, the articles provide new understandings of the many dimensions of big data ethics and politics, showing it is important to understand and increase awareness of the biases and limitations inherent in big data analysis and practices….(More)”

Startup Offers To Sequence Your Genome Free Of Charge, Then Let You Profit From It

Curated on Posted on by Stefaan Verhulst

Richard Harris at NPR: “A startup genetics company says it’s now offering to sequence your entire genome at no cost to you. In fact, you would own the data and may even be able to make money off it.

Nebula Genomics, created by the prominent Harvard geneticist George Church and his lab colleagues, seeks to upend the usual way genomic information is owned.

Today, companies like 23andMe make some of their money by scanning your genetic patterns and then selling that information to drug companies for use in research. (You choose whether to opt in.)

Church says his new enterprise leaves ownership and control of the data in an individual’s hands. And the genomic analysis Nebula will perform is much more detailed than what 23andMe and similar companies offer.

Nebula will do a full genome sequence, rather than a snapshot of key gene variants. That wider range of genetic information would makes the data more appealing to biologists and biotech and pharmaceutical companies….

Church’s approach is part of a trend that’s pushing back against the multibillion-dollar industry to buy and sell medical information. Right now, companies reap those profits and control the data.

“Patients should have the right to decide for themselves whether they want to share their medical data, and, if so, with whom,” Adam Tanner, at Harvard’s Institute for Quantitative Social Science, says in an email. “Efforts to empower people to fine-tune the fate of their medical information are a step in the right direction.” Tanner, author of a book on the subject of the trade in medical data, isn’t involved in Nebula.

The current system is “very paternalistic,” Church says. He aims to give people complete control over who gets access to their data, and let individuals decide whether or not to sell the information, and to whom.

“In this case, everything is private information, stored on your computer or a computer you designate,” Church says. It can be encrypted so nobody can read it, even you, if that’s what you want.

Drug companies interested in studying, say, diabetes patients would ask Nebula to identify people in their system who have the disease. Nebula would then identify those individuals by launching an encrypted search of participants.

People who have indicated they’re interested in selling their genetic data to a company would then be given the option of granting access to the information, along with medical data that person has designated.

Other companies are also springing up to help people control — and potentially profit from — their medical data. EncrypGen lets people offer up their genetic data, though customers have to provide their own DNA sequence. Hu-manity.co is also trying to establish a system in which people can sell their medical data to pharmaceutical companies….(More)”.

What do we learn from Machine Learning?

Curated on Posted on by Stefaan Verhulst

Blog by Giovanni Buttarelli: “…There are few authorities monitoring the impact of new technologies on fundamental rights so closely and intensively as data protection and privacy commissioners. At the International Conference of Data Protection and Privacy Commissioners, the 40th ICDPPC (which the EDPS had the honour to host), they continued the discussion on AI which began in Marrakesh two years ago with a reflection paper prepared by EDPS experts. In the meantime, many national data protection authorities have invested considerable efforts and provided important contributions to the discussion. To name only a few, the data protection authorities from Norway, France, the UK and Schleswig-Holstein have published research and reflections on AI, ethics and fundamental rights. We all see that some applications of AI raise immediate concerns about data protection and privacy; but it also seems generally accepted that there are far wider-reaching ethical implications, as a group of AI researchers also recently concluded. Data protection and privacy commissioners have now made a forceful intervention by adopting a declaration on ethics and data protection in artificial intelligence which spells out six principles for the future development and use of AI – fairness, accountability, transparency, privacy by design, empowerment and non-discrimination – and demands concerted international efforts  to implement such governance principles. Conference members will contribute to these efforts, including through a new permanent working group on Ethics and Data Protection in Artificial Intelligence.

The ICDPPC was also chosen by an alliance of NGOs and individuals, The Public Voice, as the moment to launch its own Universal Guidelines on Artificial Intelligence (UGAI). The twelve principles laid down in these guidelines extend and complement those of the ICDPPC declaration.

We are only at the beginning of this debate. More voices will be heard: think tanks such as CIPL are coming forward with their suggestions, and so will many other organisations.

At international level, the Council of Europe has invested efforts in assessing the impact of AI, and has announced a report and guidelines to be published soon. The European Commission has appointed an expert group which will, among other tasks, give recommendations on future-related policy development and on ethical, legal and societal issues related to AI, including socio-economic challenges.

As I already pointed out in an earlier blogpost, it is our responsibility to ensure that the technologies which will determine the way we and future generations communicate, work and live together, are developed in such a way that the respect for fundamental rights and the rule of law are supported and not undermined….(More)”.

Public Attitudes Toward Computer Algorithms

Curated on Posted on by Stefaan Verhulst

Aaron Smith at the Pew Research Center: “Algorithms are all around us, utilizing massive stores of data and complex analytics to make decisions with often significant impacts on humans. They recommend books and movies for us to read and watch, surface news stories they think we might find relevant, estimate the likelihood that a tumor is cancerous and predict whether someone might be a criminal or a worthwhile credit risk. But despite the growing presence of algorithms in many aspects of daily life, a Pew Research Center survey of U.S. adults finds that the public is frequently skeptical of these tools when used in various real-life situations.

This skepticism spans several dimensions. At a broad level, 58% of Americans feel that computer programs will always reflect some level of human bias – although 40% think these programs can be designed in a way that is bias-free. And in various contexts, the public worries that these tools might violate privacy, fail to capture the nuance of complex situations, or simply put the people they are evaluating in an unfair situation. Public perceptions of algorithmic decision-making are also often highly contextual. The survey shows that otherwise similar technologies can be viewed with support or suspicion depending on the circumstances or on the tasks they are assigned to do….

The following are among the major findings.

The public expresses broad concerns about the fairness and acceptability of using computers for decision-making in situations with important real-world consequences

Majorities of Americans find it unacceptable to use algorithms to make decisions with real-world consequences for humans

By and large, the public views these examples of algorithmic decision-making as unfair to the people the computer-based systems are evaluating. Most notably, only around one-third of Americans think that the video job interview and personal finance score algorithms would be fair to job applicants and consumers. When asked directly whether they think the use of these algorithms is acceptable, a majority of the public says that they are not acceptable. Two-thirds of Americans (68%) find the personal finance score algorithm unacceptable, and 67% say the computer-aided video job analysis algorithm is unacceptable….

Attitudes toward algorithmic decision-making can depend heavily on context

Despite the consistencies in some of these responses, the survey also highlights the ways in which Americans’ attitudes toward algorithmic decision-making can depend heavily on the context of those decisions and the characteristics of the people who might be affected….

When it comes to the algorithms that underpin the social media environment, users’ comfort level with sharing their personal information also depends heavily on how and why their data are being used. A 75% majority of social media users say they would be comfortable sharing their data with those sites if it were used to recommend events they might like to attend. But that share falls to just 37% if their data are being used to deliver messages from political campaigns.

Across age groups, social media users are comfortable with their data being used to recommend events - but wary of that data being used for political messaging

In other instances, different types of users offer divergent views about the collection and use of their personal data. For instance, about two-thirds of social media users younger than 50 find it acceptable for social media platforms to use their personal data to recommend connecting with people they might want to know. But that view is shared by fewer than half of users ages 65 and older….(More)”.

Quantifying Bicycle Network Connectivity in Lisbon Using Open Data

Curated on Posted on by Stefaan Verhulst

Lorena Abad and Lucas van der Meer in information: “Stimulating non-motorized transport has been a key point on sustainable mobility agendas for cities around the world. Lisbon is no exception, as it invests in the implementation of new bike infrastructure. Quantifying the connectivity of such a bicycle network can help evaluate its current state and highlight specific challenges that should be addressed. Therefore, the aim of this study is to develop an exploratory score that allows a quantification of the bicycle network connectivity in Lisbon based on open data.

For each part of the city, a score was computed based on how many common destinations (e.g., schools, universities, supermarkets, hospitals) were located within an acceptable biking distance when using only bicycle lanes and roads with low traffic stress for cyclists. Taking a weighted average of these scores resulted in an overall score for the city of Lisbon of only 8.6 out of 100 points. This shows, at a glance, that the city still has a long way to go before achieving their objectives regarding bicycle use in the city….(More)”.

You can’t characterize human nature if studies overlook 85 percent of people on Earth

Curated on Posted on by Stefaan Verhulst

Daniel Hruschka at the Conversation: “Over the last century, behavioral researchers have revealed the biases and prejudices that shape how people see the world and the carrots and sticks that influence our daily actions. Their discoveries have filled psychology textbooks and inspired generations of students. They’ve also informed how businesses manage their employees, how educators develop new curricula and how political campaigns persuade and motivate voters.

But a growing body of research has raised concerns that many of these discoveries suffer from severe biases of their own. Specifically, the vast majority of what we know about human psychology and behavior comes from studies conducted with a narrow slice of humanity – college students, middle-class respondents living near universities and highly educated residents of wealthy, industrialized and democratic nations.

Blue countries represent the locations of 93 percent of studies published in Psychological Science in 2017. Dark blue is U.S., blue is Anglophone colonies with a European descent majority, light blue is western Europe. Regions sized by population.

To illustrate the extent of this bias, consider that more than 90 percent of studies recently published in psychological science’s flagship journal come from countries representing less than 15 percent of the world’s population.

If people thought and behaved in basically the same ways worldwide, selective attention to these typical participants would not be a problem. Unfortunately, in those rare cases where researchers have reached out to a broader range of humanity, they frequently find that the “usual suspects” most often included as participants in psychology studies are actually outliers. They stand apart from the vast majority of humanity in things like how they divvy up windfalls with strangers, how they reason about moral dilemmas and how they perceive optical illusions.

Given that these typical participants are often outliers, many scholars now describe them and the findings associated with them using the acronym WEIRD, for Western, educated, industrialized, rich and democratic.

WEIRD isn’t universal

Because so little research has been conducted outside this narrow set of typical participants, anthropologists like me cannot be sure how pervasive or consequential the problem is. A growing body of case studies suggests, though, that assuming such typical participants are the norm worldwide is not only scientifically suspect but can also have practical consequences….(More)”.

The Blockchain and the New Architecture of Trust

Curated on Posted on by Stefaan Verhulst
The Blockchain and the New Architecture of Trust

Book by Kevin Werbach: “The blockchain entered the world on January 3, 2009, introducing an innovative new trust architecture: an environment in which users trust a system—for example, a shared ledger of information—without necessarily trusting any of its components. The cryptocurrency Bitcoin is the most famous implementation of the blockchain, but hundreds of other companies have been founded and billions of dollars invested in similar applications since Bitcoin’s launch. Some see the blockchain as offering more opportunities for criminal behavior than benefits to society. In this book, Kevin Werbach shows how a technology resting on foundations of mutual mistrust can become trustworthy.

The blockchain, built on open software and decentralized foundations that allow anyone to participate, seems like a threat to any form of regulation. In fact, Werbach argues, law and the blockchain need each other. Blockchain systems that ignore law and governance are likely to fail, or to become outlaw technologies irrelevant to the mainstream economy. That, Werbach cautions, would be a tragic waste of potential. If, however, we recognize the blockchain as a kind of legal technology that shapes behavior in new ways, it can be harnessed to create tremendous business and social value….(More)”