Open Data’s Effect on Food Security

Curated on Posted on by Stefaan Verhulst

Jeremy de Beer, Jeremiah Baarbé, and Sarah Thuswaldner at Open AIR: “Agricultural data is a vital resource in the effort to address food insecurity. This data is used across the food-production chain. For example, farmers rely on agricultural data to decide when to plant crops, scientists use data to conduct research on pests and design disease resistant plants, and governments make policy based on land use data. As the value of agricultural data is understood, there is a growing call for governments and firms to open their agricultural data.

Open data is data that anyone can access, use, or share. Open agricultural data has the potential to address food insecurity by making it easier for farmers and other stakeholders to access and use the data they need. Open data also builds trust and fosters collaboration among stakeholders that can lead to new discoveries to address the problems of feeding a growing population.

 

A network of partnerships is growing around agricultural data research. The Open African Innovation Research (Open AIR) network is researching open agricultural data in partnership with the Plant Phenotyping and Imaging Research Centre (P2IRC) and the Global Institute for Food Security (GIFS). This research builds on a partnership with the Global Open Data for Agriculture and Nutrition (GODAN) and they are exploring partnerships with Open Data for Development (OD4D) and other open data organizations.

…published two works on open agricultural data. Published in partnership with GODAN, “Ownership of Open Data” describes how intellectual property law defines ownership rights in data. Firms that collect data own the rights to data, which is a major factor in the power dynamics of open data. In July, Jeremiah Baarbé and Jeremy de Beer will be presenting “A Data Commons for Food Security” …The paper proposes a licensing model that allows farmers to benefit from the datasets to which they contribute. The license supports SME data collectors, who need sophisticated legal tools; contributors, who need engagement, privacy, control, and benefit sharing; and consumers who need open access….(More)“.

Teaching machines to understand – and summarize – text

Curated on Posted on by Stefaan Verhulst

 and  in The Conversation: “We humans are swamped with text. It’s not just news and other timely information: Regular people are drowning in legal documents. The problem is so bad we mostly ignore it. Every time a person uses a store’s loyalty rewards card or connects to an online service, his or her activities are governed by the equivalent of hundreds of pages of legalese. Most people pay no attention to these massive documents, often labeled “terms of service,” “user agreement” or “privacy policy.”

These are just part of a much wider societal problem of information overload. There is so much data stored – exabytes of it, as much stored as has ever been spoken by people in all of human history – that it’s humanly impossible to read and interpret everything. Often, we narrow down our pool of information by choosing particular topics or issues to pay attention to. But it’s important to actually know the meaning and contents of the legal documents that govern how our data is stored and who can see it.

As computer science researchers, we are working on ways artificial intelligence algorithms could digest these massive texts and extract their meaning, presenting it in terms regular people can understand….

Examining privacy policies

A modern internet-enabled life today more or less requires trusting for-profit companies with private information (like physical and email addresses, credit card numbers and bank account details) and personal data (photos and videos, email messages and location information).

These companies’ cloud-based systems typically keep multiple copies of users’ data as part of backup plans to prevent service outages. That means there are more potential targets – each data center must be securely protected both physically and electronically. Of course, internet companies recognize customers’ concerns and employ security teams to protect users’ data. But the specific and detailed legal obligations they undertake to do that are found in their impenetrable privacy policies. No regular human – and perhaps even no single attorney – can truly understand them.

In our study, we ask computers to summarize the terms and conditions regular users say they agree to when they click “Accept” or “Agree” buttons for online services. We downloaded the publicly available privacy policies of various internet companies, including Amazon AWS, Facebook, Google, HP, Oracle, PayPal, Salesforce, Snapchat, Twitter and WhatsApp….

Our software examines the text and uses information extraction techniques to identify key information specifying the legal rights, obligations and prohibitions identified in the document. It also uses linguistic analysis to identify whether each rule applies to the service provider, the user or a third-party entity, such as advertisers and marketing companies. Then it presents that information in clear, direct, human-readable statements….(More)”

Artificial intelligence can predict which congressional bills will pass

Curated on Posted on by Stefaan Verhulst

Matthew Hutson at Science: “The health care bill winding its way through the U.S. Senate is just one of thousands of pieces of legislation Congress will consider this year, most doomed to failure. Indeed, only about 4% of these bills become law. So which ones are worth paying attention to? A new artificial intelligence (AI) algorithm could help. Using just the text of a bill plus about a dozen other variables, it can determine the chance that a bill will become law with great precision.

Other algorithms have predicted whether a bill will survive a congressional committee, or whether the Senate or House of Representatives will vote to approve it—all with varying degrees of success. But John Nay, a computer scientist and co-founder of Skopos Labs, a Nashville-based AI company focused on studying policymaking, wanted to take things one step further. He wanted to predict whether an introduced bill would make it all the way through both chambers—and precisely what its chances were.

Nay started with data on the 103rd Congress (1993–1995) through the 113th Congress (2013–2015), downloaded from a legislation-tracking website call GovTrack. This included the full text of the bills, plus a set of variables, including the number of co-sponsors, the month the bill was introduced, and whether the sponsor was in the majority party of their chamber. Using data on Congresses 103 through 106, he trained machine-learning algorithms—programs that find patterns on their own—to associate bills’ text and contextual variables with their outcomes. He then predicted how each bill would do in the 107th Congress. Then, he trained his algorithms on Congresses 103 through 107 to predict the 108th Congress, and so on.

Nay’s most complex machine-learning algorithm combined several parts. The first part analyzed the language in the bill. It interpreted the meaning of words by how they were embedded in surrounding words. For example, it might see the phrase “obtain a loan for education” and assume “loan” has something to do with “obtain” and “education.” A word’s meaning was then represented as a string of numbers describing its relation to other words. The algorithm combined these numbers to assign each sentence a meaning. Then, it found links between the meanings of sentences and the success of bills that contained them. Three other algorithms found connections between contextual data and bill success. Finally, an umbrella algorithm used the results from those four algorithms to predict what would happen…. his program scored about 65% better than simply guessing that a bill wouldn’t pass, Nay reported last month in PLOS ONE…(More).

Why blockchain could be your next form of ID as a world citizen

Curated on Posted on by Stefaan Verhulst

 at TechRepublic: “Blockchain is moving from banking to the refugee crisis, as Microsoft and Accenture on Monday announced a partnership to use the technology to provide a legal form of identification for 1.1 billion people worldwide as part of the global public-private partnership ID2020.

The two tech giants developed a prototype that taps Accenture’s blockchain capabilities and runs on Microsoft Azure. The tech tool uses a person’s biometric data, such as a fingerprint or iris scan, to unlock the record-keeping blockchain technology and create a legal ID. This will allow refugees to have a personal identity record they can access from an app on a smartphone to receive assistance at border crossings, or to access basic services such as healthcare, according to a press release.

The prototype is designed so that personally identifiable information (PII) always exists “off chain,” and is not stored in a centralized system. Citizens use their biometric data to access their information, and chose when to share it—preventing the system from being accessed by tyrannical governments that refugees are fleeing from, as ZDNet noted.

Accenture’s platform is currently used in the Biometric Identity Management System operated by the United Nations High Commissioner for Refugees, which has enrolled more than 1.3 million refugees in 29 nations across Asia, Africa, and the Caribbean. The system is predicted to support more than 7 million refugees from 75 countries by 2020, the press release noted.

“People without a documented identity suffer by being excluded from modern society,” said David Treat, a managing director in Accenture’s global blockchain business, in the press release. “Our prototype is personal, private and portable, empowering individuals to access and share appropriate information when convenient and without the worry of using or losing paper documentation.”

ID is key for accessing education, healthcare, voting, banking, housing, and other family benefits, the press release noted. ID2020’s goal is to create a secure, established digital ID system for all citizens worldwide….

Blockchain will likely play an increasing role in both identification and security moving forward, especially as it relates to the Internet of Things (IoT). For example, Telstra, an Australian telecommunications company, is currently experimenting with a combination of blockchain and biometric security for its smart home products, ZDNet reported….(More)”.

AI software created for drones monitors wild animals and poachers

Curated on Posted on by Stefaan Verhulst

Springwise: “Artificial intelligence software installed into drones is to be used by US tech company Neurala to help protect endangered species from poachers. Working with the region’s Lingbergh Foundation, Neurala is currently helping operations in South Africa, Malawi and Zimbabwe and have had requests from Botswana, Mozambique and Zambia for assistance with combatting poaching.

The software is designed to monitor video as it is streamed back to researchers from unmanned drones that can fly for up to five hours, identifying animals, vehicles and poachers in real time without any human input. It can then alert rangers via the mobile command center if anything out of the ordinary is detected. The software can analyze regular or infrared footage, and therefore works with video taken day or night.

The Lindbergh Foundation will be deploying the technology as part of operation Air Shepherd, which is aimed at protecting elephants and rhinos in Southern Africa from poachers. According to the Foundation, elephants and rhinos are at risk of being extinct in just 10 years if current poaching rates continue, and has logged 5,000 hours of drone flight time over the course of 4,000 missions to date.

The use of drones within business models is proving popular, with recent innovations including a drone painting systemthat created crowdfunded murals and two Swiss hospitals that used a drone to deliver lab samples between them….(More)”.

LSE launches crowdsourcing project inspiring millennials to shape Brexit

Curated on Posted on by Stefaan Verhulst

LSE Press Release: “A crowdsourcing project inspiring millennials in Britain and the EU to help shape the upcoming Brexit negotiations is being launched by the London School of Economics and Political Science (LSE) this week.

The social media-based project, which hopes to engage 3000 millennials aged 35 and under, kicks off on 23 June, the first anniversary of the life-changing vote to take Britain out of the EU.

One of the Generation Brexit project leaders, Dr Jennifer Jackson-Preece from LSE’s European Institute, said the online platform would give a voice to British and European millennials on the future of Europe in the Brexit negotiations and beyond.

She said: “We’re going to invite millennials from across the UK and Europe to debate, decide and draft policy proposals that will be sent to parliaments in Westminster and Strasbourg during the negotiations.”

Another project leader, Dr Roch Dunin-Wąsowicz, said the pan-European project would seek views from a whole cross section of millennials, including Leavers, Remainers, left and right-wingers, European federalists and nationalists.

“We want to come up with millennial proposals for a mutually beneficial relationship, reflecting the diverse political, cultural, religious and economic backgrounds in the UK and EU.

“We are especially keen to engage the forgotten, the apolitical and the apathetic – for whom Brexit has become a moment of political awakening,” he said.

Generation Brexit follows on the heels of LSE’s Constitution UK crowdsourcing project in 2015, which broke new ground in galvanising people around the country to help shape Britain’s first constitution. The 10-week internet project signed up 1500 people from all corners of the UK to debate how the country should be governed.

Dr Manmit Bhambra, also working on the project, said the success of the Constitution UK platform had laid the foundation for Generation Brexit, with LSE hoping to double the numbers and sign up 3000 participants, split equally between Britain and Europe.

The project can be accessed at www.generationbrexit.org and all updates will be available on Twitter @genbrexit & @lsebrexitvote with the hashtag #GenBrexit, and on facebook.com/GenBrexit… (More)”.

Remix, Slang and Memes: A New Collection Documents Web Culture

Curated on Posted on by Stefaan Verhulst

 at the Library of Congress: “…just announced the release of the Web Cultures Web Archive Collection, a representative sampling of websites documenting the creation and sharing of emergent cultural traditions on the web.

Why is this important? Increasingly, people take to their smart phones, tablets and laptops to enact much of their lives through creative communication, making the web a predominant place to share folklore. It is where a significant portion of the historical record is now being written.

Archived from the web starting in 2014, the new—and growing—collection of collaborative cultural creation includes reaction GIFs (animated images, often bodies in motion, used online as responses or reactions to previous posts in a communication thread); image macros (photographic images on which a funny caption is superimposed); and memes (in this context, internet phenomena).

Because the collection aims to document online communities that have established, shaped and disseminated communication tropes and themes, it also includes sites that capture icon-based communications, such as emoji, and those that establish or define vernacular language. Examples of these include “Leet” and “Lolspeak,” two examples of written English language that derive from internet usages. Leet emerged from 1980s software piracy communities, referring to “elite” code wranglers. Lolspeak primarily features in memes and carries collective meaning as the form of English that cats might use.

Some sites represent the DIY (do-it-yourself) movements of crafting and making—for example, Instructables. Still others focus on the distribution and discussion of digital “urban legends” and lore, such as Creepypasta, or vernacular creative forms, such as fan fiction.

The project is a contemporary manifestation of the AFC’s mission to document traditional cultural forms and practices, and results from the collaborative work between the AFC and those steeped in digital culture, both scholars and enthusiasts….(More)”.

Does democracy cause innovation? An empirical test of the popper hypothesis

Curated on Posted on by Stefaan Verhulst

Yanyan Gao, Leizhen Zang, Antoine Roth, and Puqu Wang in Research Policy: “Democratic countries produce higher levels of innovation than autocratic ones, but does democratization itself lead to innovation growth either in the short or in the long run? The existing literature has extensively examined the relationship between democracy and growth but seldom explored the effect of democracy on innovation, which might be an important channel through which democracy contributes to economic growth. This article aims to fill this gap and contribute to the long-standing debate on the relationship between democracy and innovation by offering empirical evidence based on a data set covering 156 countries between 1964 and 2010. The results from the difference-in-differences method show that democracy itself has no direct positive effect on innovation measured with patent counts, patent citations and patent originality….(More)”.

 

Towards Crowd-Scale Deliberation

Curated on Posted on by Stefaan Verhulst

Paper by Mark Klein: “Let us define deliberation as the activity where groups of people (1) identify possible solutions for a problem, (2) evaluate these alternatives, and (3) select the solution(s) that best meet their needs. Deliberation processes have changed little in centuries. Typically, small groups of powerful players craft policies behind closed doors, and then battle to engage wider support for their preferred options. Most people affected by the decisions have at best limited input into defining the solution options. This approach has become increasingly inadequate as the scale and complexity of the problems we face has increased. Many important ideas and perspectives simply do not get incorporated, squandering the opportunity for far superior outcomes. We have the potential to do much better by radically widening the circle of people involved in complex deliberations, moving from “team” scales (tens of participants) to “crowd” scales (hundreds, thousands, or more).

This is because crowd-scale interactions have been shown to produce, in appropriate circumstances, such powerful emergent phenomena as:

  • The long tail: crowd-scale participation enables access to a much greater diversity of ideas than would otherwise be practical: potentially superior solutions “small voices” (the tail of the frequency distribution) have a chance to be heard .
  • Idea synergy: the ability for users to share their creations in a common forum can enable a synergistic explosion of creativity, since people often develop new ideas by forming novel combinations and extensions of ideas that have been put out by others.
  • Many eyes: crowds can produce remarkably high-quality results (e.g. in open source software) by virtue of the fact that there are multiple independent verifications – many eyes continuously checking the shared content for errors and correcting them .
  • Wisdom of the crowds: large groups of (appropriately independent, motivated and informed) contributors can collectively make better judgments than those produced by the individuals that make them up, often exceeding the performance of experts,because their collective judgment cancels out the biases and gaps of the individual members…

Our team has been developing crowd-scale deliberation support technologies that address these three fundamental challenges by enabling:

  • better ideation: helping crowds develop better solution ideas
  • better evaluation: helping crowds evaluate potential solutions more accurately
  • better decision-making: helping crowds select pareto-optimal solutions…(More)”.

How did awful panel discussions become the default format?

Curated on Posted on by Stefaan Verhulst

 at The Guardian: “With the occasional exception, my mood in conferences usually swings between boredom, despair and rage. The turgid/self-aggrandizing keynotes and coma-inducing panels, followed by people (usually men) asking ‘questions’ that are really comments, and usually not on topic. The chairs who abdicate responsibility and let all the speakers over-run, so that the only genuinely productive bit of the day (networking at coffee breaks and lunch) gets squeezed. I end up dozing off, or furiously scribbling abuse in my notebook as a form of therapy, and hoping my neighbours can’t see what I’m writing. I probably look a bit unhinged…

This matters both because of the lost opportunity that badly run conferences represent, and because they cost money and time. I hope that if it was easy to fix, people would have done so already, but the fact is that the format is tired and unproductive.

For example, how did something as truly awful as panel discussions become the default format? They end up being a parade of people reading out papers, or they include terrible powerpoints crammed with too many words and illegible graphics. Can we try other formats, like speed dating (eg 10 people pitch their work for 2 minutes each, then each goes to a table and the audience hooks up (intellectually, I mean) with the ones they were interested in); world cafes; simulation games; joint tasks (eg come up with an infographic that explains X)? Anything, really. Yes ‘manels’ (male only panels – take the pledge here) are an outrage, but why not go for complete abolition, rather than mere gender balance?

Conferences frequently discuss evidence and results. So where is the evidence and results for the efficacy of conferences? Given the resources being ploughed into research on development (DFID alone spends about £350m a year), surely it would be a worthwhile investment, if it hasn’t already been done, to sponsor a research programme that runs multiple parallel experiments with different event formats, and compares the results in terms of participant feedback, how much people retain a month after the event etc? At the very least, can they find or commission a systematic review on what the existing evidence says?

Feedback systems could really help. A public eBay-type ratings system to rank speakers/conferences would provide nice examples of good practice for people to draw on (and bad practice to avoid). Or why not go real-time and encourage instant audience feedback? OK, maybe Occupy-style thumbs up from the audience if they like the speaker, thumbs down if they don’t would be a bit in-your-face for academe, but why not introduce a twitterwall to encourage the audience to interact with the speaker (perhaps with moderation to stop people testing the limits, as my LSE students did to Owen Barder last term)?

We need to get better at shaping the format to fit the the precise purpose of the conference. … if the best you can manage is ‘disseminating new research’ of ‘information sharing’, alarm bells should probably ring….(More)”.