Paper by Lauren Henry Scholz: “Generalist confusion about the technology behind complex algorithms has led to inconsistent case law for algorithmic contracts. Case law explicitly grounded in the principle that algorithms are constructive agents for the companies they serve would provide a clear basis for enforceability of algorithmic contracts that is both principled from a technological perspective and is readily intelligible and able to be applied by generalists….(More)”.
Car Data Facts
About: “Welcome to CarDataFacts.eu! This website provides a fact-based overview on everything related to the sharing of vehicle-generated data with third parties. Through a series of educational infographics, this website answers the most common questions about access to car data in a clear and simple way.
CarDataFacts.eu also addresses consumer concerns about sharing data in a safe and a secure way, as well as explaining some of the complex and technical terminology surrounding the debate.
CarDataFacts.eu is brought to you by ACEA, the European Automobile Manufacturers’ Association, which represents the 15 Europe-based car, van, truck and bus makers….(More)”.
Blockchain Governance: What We Can Learn From the Economics of Corporate Governance
Paper by Darcy W E Allen and Chris Berg: ” Understanding the considerations and complexities of blockchain governance is urgent. The aim of this paper is to draw on institutional governance theory — including corporate governance — to provide insights into the core considerations in designing blockchain governance mechanisms.
We define blockchain governance are the processes by which stakeholders (those who are affected by and can affect the network) exercise bargaining power over the network. The main considerations include how we define stakeholders in blockchain governance, how the consensus mechanism itself distributes endogenous bargaining power between those stakeholders, the role of exogenous governance mechanisms and institutional frameworks, and the needs for bootstrapping. While we can learn from corporate and internet governance, blockchain governance should be understood as being an institutionally distinct organisational form with distinct governance systems….(More)”.
Invest 5% of research funds in ensuring data are reusable
Barend Mons at Nature: “It is irresponsible to support research but not data stewardship…
Many of the world’s hardest problems can be tackled only with data-intensive, computer-assisted research. And I’d speculate that the vast majority of research data are never published. Huge sums of taxpayer funds go to waste because such data cannot be reused. Policies for data reuse are falling into place, but fixing the situation will require more resources than the scientific community is willing to face.
In 2013, I was part of a group of Dutch experts from many disciplines that called on our national science funder to support data stewardship. Seven years later, policies that I helped to draft are starting to be put into practice. These require data created by machines and humans to meet the FAIR principles (that is, they are findable, accessible, interoperable and reusable). I now direct an international Global Open FAIR office tasked with helping communities to implement the guidelines, and I am convinced that doing so will require a large cadre of professionals, about one for every 20 researchers.
Even when data are shared, the metadata, expertise, technologies and infrastructure necessary for reuse are lacking. Most published data sets are scattered into ‘supplemental files’ that are often impossible for machines or even humans to find. These and other sloppy data practices keep researchers from building on each other’s work. In cases of disease outbreaks, for instance, this might even cost lives….(More)”.
Facial Recognition Software requires Checks and Balances
David Eaves, and Naeha Rashid in Policy Options: “A few weeks ago, members of the Nexus traveller identification program were notified that Canadian Border Services is upgrading its automated system, from iris scanners to facial recognition technology. This is meant to simplify identification and increase efficiency without compromising security. But it also raises profound questions concerning how we discuss and develop public policies around such technology – questions that may not be receiving sufficiently open debate in the rush toward promised greater security.
Analogous to the U.S. Customs and Border Protection (CBP) program Global Entry, Nexus is a joint Canada-US border control system designed for low-risk, pre-approved travellers. Nexus does provide a public good, and there are valid reasons to improve surveillance at airports. Even before 9/11, border surveillance was an accepted annoyance and since then, checkpoint operations have become more vigilant and complex in response to the public demand for safety.
Nexus is one of the first North America government-sponsored services to adopt facial recognition, and as such it could be a pilot program that other services will follow. Left unchecked, the technology will likely become ubiquitous at North American border crossings within the next decade, and it will probably be adopted by governments to solve domestic policy challenges.
Facial recognition software is imperfect and has documented bias, but it will continue to improve and become superior to humans in identifying individuals. Given this, questions arise such as, what policies guide the use of this technology? What policies should inform future government use? In our headlong rush toward enhanced security, we risk replicating the justification the used by the private sector in an attempt to balance effectiveness, efficiency and privacy.
One key question involves citizens’ capacity to consent. Previously, Nexus members submitted to fingerprint and retinal scans – biometric markers that are relatively unique and enable government to verify identity at the border. Facial recognition technology uses visual data and seeks, analyzes, and stores identifying facial information in a database, which is then used to compare with new images and video….(More)”.
Tesco Grocery 1.0, a large-scale dataset of grocery purchases in London
Paper by Luca Maria Aiello, Daniele Quercia, Rossano Schifanella & Lucia Del Prete: “We present the Tesco Grocery 1.0 dataset: a record of 420 M food items purchased by 1.6 M fidelity card owners who shopped at the 411 Tesco stores in Greater London over the course of the entire year of 2015, aggregated at the level of census areas to preserve anonymity. For each area, we report the number of transactions and nutritional properties of the typical food item bought including the average caloric intake and the composition of nutrients.
The set of global trade international numbers (barcodes) for each food type is also included. To establish data validity we: i) compare food purchase volumes to population from census to assess representativeness, and ii) match nutrient and energy intake to official statistics of food-related illnesses to appraise the extent to which the dataset is ecologically valid. Given its unprecedented scale and geographic granularity, the data can be used to link food purchases to a number of geographically-salient indicators, which enables studies on health outcomes, cultural aspects, and economic factors….(More)”.
How big data is dividing the public in China’s coronavirus fight – green, yellow, red
Article by Viola Zhou: “On Valentine’s Day, a 36-year-old lawyer Matt Ma in the eastern Chinese province of Zhejiang discovered he had been coded “red”.The colour, displayed in a payment app on his smartphone, indicated that he needed to be quarantined at home even though he had no symptoms of the dangerous coronavirus.
Without a green light from the system, Ma could not travel from his ancestral hometown of Lishui to his new home city of Hangzhou, which is now surrounded by checkpoints set up to contain the epidemic.
Ma is one of the millions of people whose movements are being choreographed by the government through software that feeds on troves of data and issues orders that effectively dictate whether they must stay in or can go to work.Their experience represents a slice of China’s desperate attempt to stop the coronavirus by using a mixed bag of cutting-edge technologies and old-fashioned surveillance. It was also a rare real-world test of the use of technology on a large scale to halt the spread of communicable diseases.
“This kind of massive use of technology is unprecedented,” said Christos Lynteris, a medical anthropologist at the University of St Andrews who has studied epidemics in China.
But Hangzhou’s experiment has also revealed the pitfalls of applying opaque formulas to a large population.
In the city’s case, there are reports of people being marked incorrectly, falling victim to an algorithm that is, by the government’s own admission, not perfect….(More)”.
Accelerating AI with synthetic data
Essay by Khaled El Emam: “The application of artificial intelligence and machine learning to solve today’s problems requires access to large amounts of data. One of the key obstacles faced by analysts is access to this data (for example, these issues were reflected in reports from the General Accountability Office and the McKinsey Institute).
Synthetic data can help solve this data problem in a privacy preserving manner.
What is synthetic data ?
Data synthesis is an emerging privacy-enhancing technology that can enable access to realistic data, which is information that may be synthetic, but has the properties of an original dataset. It also simultaneously ensures that such information can be used and disclosed with reduced obligations under contemporary privacy statutes. Synthetic data retains the statistical properties of the original data. Therefore, there are an increasing number of use cases where it would serve as a proxy for real data.
Synthetic data is created by taking an original (real) dataset and then building a model to characterize the distributions and relationships in that data — this is called the “synthesizer.” The synthesizer is typically an artificial neural network or other machine learning technique that learns these (original) data characteristics. Once that model is created, it can be used to generate synthetic data. The data is generated from the model and does not have a 1:1 mapping to real data, meaning that the likelihood of mapping the synthetic records to real individuals would be very small — it is not considered personal information.
Many different types of data can be synthesized, including images, video, audio, text and structured data. The main focus in this article is on the synthesis of structured data.
Even though data can be generated in this manner, that does not mean it cannot be personal information. If the synthesizer is overfit to real data, then the generated data will replicate the original real data. Therefore, the synthesizer has to be constructed in a manner to avoid such overfitting. A formal privacy assurance should also be performed on the synthesized data to validate that there is a weak mapping between synthetic records to individuals….(More)”.
Monitoring of the Venezuelan exodus through Facebook’s advertising platform
Paper by Palotti et al: “Venezuela is going through the worst economical, political and social crisis in its modern history. Basic products like food or medicine are scarce and hyperinflation is combined with economic depression. This situation is creating an unprecedented refugee and migrant crisis in the region. Governments and international agencies have not been able to consistently leverage reliable information using traditional methods. Therefore, to organize and deploy any kind of humanitarian response, it is crucial to evaluate new methodologies to measure the number and location of Venezuelan refugees and migrants across Latin America.
In this paper, we propose to use Facebook’s advertising platform as an additional data source for monitoring the ongoing crisis. We estimate and validate national and sub-national numbers of refugees and migrants and break-down their socio-economic profiles to further understand the complexity of the phenomenon. Although limitations exist, we believe that the presented methodology can be of value for real-time assessment of refugee and migrant crises world-wide….(More)”.
The Economic Impact of Open Data: Opportunities for value creation in Europe
Press Release: “The European Data Portal publishes its study “The Economic Impact of Open Data: Opportunities for value creation in Europe”. It researches the value created by open data in Europe. It is the second study by the European Data Portal, following the 2015 report. The open data market size is estimated at €184 billion and forecast to reach between €199.51 and €334.21 billion in 2025. The report additionally considers how this market size is distributed along different sectors and how many people are employed due to open data. The efficiency gains from open data, such as potential lives saved, time saved, environmental benefits, and improvement of language services, as well as associated potential costs savings are explored and quantified where possible. Finally, the report also considers examples and insights from open data re-use in organisations. The key findings of the report are summarised below:
- The specification and implementation of high-value datasets as part of the new Open Data Directive is a promising opportunity to address quality & quantity demands of open data.
- Addressing quality & quantity demands is important, yet not enough to reach the full potential of open data.
- Open data re-users have to be aware and capable of understanding and leveraging the potential.
- Open data value creation is part of the wider challenge of skill and process transformation: a lengthy process whose change and impact are not always easy to observe and measure.
- Sector-specific initiatives and collaboration in and across private and public sector foster value creation.
- Combining open data with personal, shared, or crowdsourced data is vital for the realisation of further growth of the open data market.
- For different challenges, we must explore and improve multiple approaches of data re-use that are ethical, sustainable, and fit-for-purpose….(More)”.
