Coastal research increasingly depends on citizen scientists


Brenna Visser at CS Monitor: “…This monthly ritual is a part of the COASST survey, a program that relies on data taken by volunteers to study large-scale patterns in seabird populations on the West Coast. The Haystack Rock Awareness Program conducts similar surveys for sea stars and marine debris throughout the year.

Surveys like these play a small part in a growing trend in the science community to use citizen scientists as a way to gather massive amounts of data. Over the weekend, marine scientists and conservationists came to Cannon Beach for an annual Coast Conference, a region wide event to discuss coastal science and stewardship.

Whether the presentation was about ocean debris, marine mammals, seabirds, or ocean jellies, many relied on the data collection work of volunteers throughout the state. A database for citizen science programs called Citsci.org, which recorded only a few dozen groups 10 years ago, now has more than 500 groups registered across the country, with new ones registering every day….

Part of the rise has to do with technology, she said. Apps that help identify species and allow unprecedented access to information have driven interest up and removed barriers that would have otherwise made it harder to collect data without formal training. Another is the science community slowly coming around to accept citizen science.

“I think there’s a lot of reticence in the science community to use citizen science. There’s some doubt the data collected is of the precision or accuracy that is needed to document phenomena,” Parrish said. “But as it grows, the more standardized it becomes. What we’re seeing right now is a lot of discussion in citizen science programs asking what they need to do to get to that level.”…While a general decline in federal funding for scientific research could play a factor in the science community’s acceptance of using volunteer-collected data, Parrish said, regardless of funding, there are some projects only citizen scientists can accomplish….(More)”

Say goodbye to the information age: it’s all about reputation now


Gloria Origgi at Aeon: “There is an underappreciated paradox of knowledge that plays a pivotal role in our advanced hyper-connected liberal democracies: the greater the amount of information that circulates, the more we rely on so-called reputational devices to evaluate it. What makes this paradoxical is that the vastly increased access to information and knowledge we have today does not empower us or make us more cognitively autonomous. Rather, it renders us more dependent on other people’s judgments and evaluations of the information with which we are faced.

We are experiencing a fundamental paradigm shift in our relationship to knowledge. From the ‘information age’, we are moving towards the ‘reputation age’, in which information will have value only if it is already filtered, evaluated and commented upon by others. Seen in this light, reputation has become a central pillar of collective intelligence today. It is the gatekeeper to knowledge, and the keys to the gate are held by others. The way in which the authority of knowledge is now constructed makes us reliant on what are the inevitably biased judgments of other people, most of whom we do not know.

Let me give some examples of this paradox. If you are asked why you believe that big changes in the climate are occurring and can dramatically harm future life on Earth, the most reasonable answer you’re likely to provide is that you trust the reputation of the sources of information to which you usually turn for acquiring information about the state of the planet. In the best-case scenario, you trust the reputation of scientific research and believe that peer-review is a reasonable way of sifting out ‘truths’ from false hypotheses and complete ‘bullshit’ about nature. In the average-case scenario, you trust newspapers, magazines or TV channels that endorse a political view which supports scientific research to summarise its findings for you. In this latter case, you are twice-removed from the sources: you trust other people’s trust in reputable science….(More)”.

Follow the Money: How to Track Federal Funding to Local Governments


Research Report by Megan RandallTracy GordonSolomon Greene and Erin Huffer: “To respond effectively to state and federal policy changes, city leaders, non-profit service providers, advocates, and researchers all need accurate data on how federal funds flow to local governments. Unfortunately, those data are spread across multiple sources that are often indecipherable or inaccessible to non-experts. The purpose of this guide is to help data users navigate the patchwork of primary data sources and online portals that show how the federal government distributes funding to local governments. We drew on the literature, an inventory of online resources, interviews with local and federal officials, and Urban Institute research staff experience to catalog available data on federal-local transfers. We describe the strengths, weaknesses, and best uses of various data sources and portals and provide guidance on where users can find information to understand trends or how their community stands relative to its peers. Our guide concludes with simple recommendations for how to improve data quality, comparability, and usability at all levels of government….(More)”.

Could the open government movement shut the door on Freedom of Information


 and  in The Conversation: “For democracy to work, citizens need to know what their government is doing. Then they can hold government officials and institutions accountable.

Over the last 50 years, Freedom of Information – or FOI – laws have been one of the most useful methods for citizens to learn what government is doing. These state and federal laws give people the power to request, and get, government documents. From everyday citizens to journalists, FOI laws have proven a powerful way to uncover the often-secret workings of government.

But a potential threat is emerging – from an unexpected place – to FOI laws.

We are scholars of government administration, ethics and transparency. And our research leads us to believe that while FOI laws have always faced many challenges, including resistance, evasion,  and poor implementation and enforcement, the last decade has brought a different kind of challenge in the form of a new approach to transparency….

The open government movement could help FOI implementation. Government information posted online, which is a core goal of open government advocates, can reduce the number of FOI requests. Open government initiatives can explicitly promote FOI by encouraging the passage of FOI laws, offering more training for officials who fill FOI requests, and developing technologies to make it easier to process and track FOI requests.

On the other hand, the relationship between open government and FOI may not always be positive in practice.

First, as with all kinds of public policy issues, resources – both money and political attention – are inherently scarce. Government officials now have to divide their attention between FOI and other open government initiatives. And funders now have to divide their financial resources between FOI and other open government initiatives.

Second, the open government reform movement as well as the FOI movement have long depended on nonprofit advocacy groups – from the National Freedom of Information Coalition and its state affiliates to the Sunlight Foundation – to obtain and disseminate government information. This means that the financial stability of those nonprofit groups is crucial. But their efforts, as they grow, may each only get a shrinking portion of the total amount of grant money available. Freedominfo.org, a website for gathering and comparing information on FOI laws around the world, had to suspend its operations in 2017 due to resources drying up.

We believe that priorities among government officials and good government advocates may also shift away from FOI. At a time when open data is “hot,” FOI programs could get squeezed as a result of this competition. Further, by allowing governments to claim credit for more politically convenient reforms such as online data portals, the open government agenda may create a false sense of transparency – there’s a lot more government information that isn’t available in those portals.

This criticism was leveled recently against Kenya, whose government launched a high-profile open data portal for publishing data on government performance and activities in 2011, yet delayed passage of an FOI law until 2016.

Similarly, in the United Kingdom, one government minister said in 2012,“I’d like to make Freedom of Information redundant, by pushing out so much data that people won’t have to ask for it.”…(More)”

When Fighting Fake News Aids Censorship


Courtney C. Radsch at Project Syndicate: “Many media analysts have rightly identified the dangers posed by “fake news,” but often overlook what the phenomenon means for journalists themselves. Not only has the term become a shorthand way to malign an entire industry; autocrats are invoking it as an excuse to jail reporters and justify censorship, often on trumped-up charges of supporting terrorism.

Around the world, the number of honest journalists jailed for publishing fake or fictitious news is at an all-time high of at least 21. As non-democratic leaders increasingly use the “fake news” backlash to clamp down on independent media, that number is likely to climb.

The United States, once a world leader in defending free speech, has retreated from this role. President Donald Trump’s Twitter tirades about “fake news” have given autocratic regimes an example by which to justify their own media crackdowns. In December, China’s state-run People’s Daily newspaper posted tweets and a Facebook post welcoming Trump’s fake news mantra, noting that it “speaks to a larger truth about Western media.” This followed the Egyptian government’s praise for the Trump administration in February 2017, when the country’s foreign ministry criticized Western journalists for their coverage of global terrorism.

And in January 2017, Turkish President Recep Tayyip Erdoğan praised Trump for berating a CNN reporter during a live news conference. Erdoğan, who criticized the network for its coverage of pro-democracy protests in Turkey in 2013, said that Trump had put the journalist “in his place.” Trump returned the compliment when he met Erdoğan a few months later. Praising his counterpart for being an ally in the fight against terrorism, Trump made no mention of Erdoğan’s own dismal record on press freedom.

It is no accident that these three countries have been quickest to embrace Trump’s “fake news” trope. China, Egypt, and Turkey jailed more than half of the world’s journalists in 2017, continuing a trend from the previous year. The international community’s silence in the face of these governments’ attacks on independent media seems to have been interpreted as consent….(More)”.

Global Fishing Watch And The Power Of Data To Understand Our Natural World


A year and a half ago I wrote about the public debut of the Global Fishing Watch project as a showcase of what becomes possible when massive datasets are made accessible to the general public through easy-to-use interfaces that allow them to explore the planet they inhabit. At the time I noted how the project drove home the divide between the “glittering technological innovation of Silicon Valley and the technological dark ages of the development community” and what becomes possible when technologists and development organizations come together to apply incredible technology not for commercial gain, but rather to save the world itself. Continuing those efforts, last week Global Fishing Watch launched what it describes as the “the first ever dataset of global industrial fishing activities (all countries, all gears),” making the entire dataset freely accessible to seed new scientific, activist, governmental, journalistic and citizen understanding of the state of global fishing.

The Global Fishing Watch project stands as a powerful model for data-driven development work done right and hopefully, the rise of notable efforts like it will eventually catalyze the broader development community to emerge from the stone age of technology and more openly embrace the technological revolution. While it has a very long way to go, there are signs of hope for the development community as pockets of innovation begin to infuse the power of data-driven decision making and situational awareness into everything from disaster response to proactive planning to shaping legislative action.

Bringing technologists and development organizations together is not always that easy and the most creative solutions aren’t always to be found among the “usual suspects.” Open data and open challenges built upon them offer the potential for organizations to reach beyond the usual communities they interact with and identify innovative new approaches to the grand challenges of their fields. Just last month a collaboration of the World Bank, WeRobotics and OpenAerialMap launched a data challenge to apply deep learning to assess aerial imagery in the immediate aftermath of disasters to determine the impact to food producing trees and to road networks. By launching the effort as an open AI challenge, the goal is to reach the broader AI and open development communities at the forefront of creative and novel algorithmic approaches….(More)”.

How AI-Driven Insurance Could Reduce Gun Violence


Jason Pontin at WIRED: “As a political issue, guns have become part of America’s endless, arid culture wars, where Red and Blue tribes skirmish for political and cultural advantage. But what if there were a compromise? Economics and machine learning suggest an answer, potentially acceptable to Americans in both camps.

Economists sometimes talk about “negative externalities,” market failures where the full costs of transactions are borne by third parties. Pollution is an externality, because society bears the costs of environmental degradation. The 20th-century British economist Arthur Pigou, who formally described externalities, also proposed their solution: so-called “Pigovian taxes,” where governments charge producers or customers, reducing the quantity of the offending products and sometimes paying for ameliorative measures. Pigovian taxes have been used to fight cigarette smoking or improve air quality, and are the favorite prescription of economists for reducing greenhouse gases. But they don’t work perfectly, because it’s hard for governments to estimate the costs of externalities.

Gun violence is a negative externality too. The choices of millions of Americans to buy guns overflow into uncaptured costs for society in the form of crimes, suicides, murders, and mass shootings. A flat gun tax would be a blunt instrument: It could only reduce gun violence by raising the costs of gun ownership so high that almost no one could legally own a gun, which would swell the black market for guns and probably increase crime. But insurers are very good at estimating the risks and liabilities of individual choices; insurance could capture the externalities of gun violence in a smarter, more responsive fashion.

Here’s the proposed compromise: States should require gun owners to be licensed and pay insurance, just as car owners must be licensed and insured today….

The actuaries who research risk have always considered a wide variety of factors when helping insurers price the cost of a policy. Car, home, and life insurance can vary according to a policy holder’s age, health, criminal record, employment, residence, and many other variables. But in recent years, machine learning and data analytics have provided actuaries with new predictive powers. According to Yann LeCun, the director of artificial intelligence at Facebook and the primary inventor of an important technique in deep learning called convolution, “Deep learning systems provide better statistical models with enough data. They can be advantageously applied to risk evaluation, and convolutional neural nets can be very good at prediction, because they can take into account a long window of past values.”

State Farm, Liberty Mutual, Allstate, and Progressive Insurance have all used algorithms to improve their predictive analysis and to more accurately distribute risk among their policy holders. For instance, in late 2015, Progressive created a telematics app called Snapshot that individual drivers used to collect information on their driving. In the subsequent two years, 14 billion miles of driving data were collected all over the country and analyzed on Progressive’s machine learning platform, H20.ai, resulting in discounts of $600 million for their policy holders. On average, machine learning produced a $130 discount for Progressive customers.

When the financial writer John Wasik popularized gun insurance in a series of posts in Forbes in 2012 and 2013, the NRA’s argument about prior constraints was a reasonable objection. Wasik proposed charging different rates to different types of gun owners, but there were too many factors that would have to be tracked over too long a period to drive down costs for low-risk policy holders. Today, using deep learning, the idea is more practical: Insurers could measure the interaction of dozens or hundreds of factors, predicting the risks of gun ownership and controlling costs for low-risk gun owners. Other, more risky bets might pay more. Some very risky would-be gun owners might be unable to find insurance at all. Gun insurance could even be dynamically priced, changing as the conditions of the policy holders’ lives altered, and the gun owners proved themselves better or worse risks.

Requiring gun owners to buy insurance wouldn’t eliminate gun violence in America. But a political solution to the problem of gun violence is chimerical….(More)”.

Prediction, Judgment and Complexity


NBER Working Paper by Agrawal, Ajay and Gans, Joshua S. and Goldfarb, Avi: “We interpret recent developments in the field of artificial intelligence (AI) as improvements in prediction technology. In this paper, we explore the consequences of improved prediction in decision-making. To do so, we adapt existing models of decision-making under uncertainty to account for the process of determining payoffs. We label this process of determining the payoffs ‘judgment.’ There is a risky action, whose payoff depends on the state, and a safe action with the same payoff in every state. Judgment is costly; for each potential state, it requires thought on what the payoff might be. Prediction and judgment are complements as long as judgment is not too difficult. We show that in complex environments with a large number of potential states, the effect of improvements in prediction on the importance of judgment depend a great deal on whether the improvements in prediction enable automated decision-making. We discuss the implications of improved prediction in the face of complexity for automation, contracts, and firm boundaries….(More)”.

A primer on political bots: Part one


Stuart W. Shulman et al at Data Driven Journalism: “The rise of political bots brings into sharp focus the role of automated social media accounts in today’s democratic civil society. Events during the Brexit referendum and the 2016 U.S. Presidential election revealed the scale of this issue for the first time to the majority of citizens and policy-makers. At the same time, the deployment of Russian-linked bots designed to promote pro-gun laws in the aftermath of the Florida school shooting demonstrates the state-sponsored, real-time readiness to shape, through information warfare, the dominant narratives on platforms such as Twitter. The regular news reports on these issues lead us to conclude that the foundations of democracy have become threatened by the presence of aggressive and socially disruptive bots, which aim to manipulate online political discourse.

While there is clarity on the various functions that bot accounts can be scripted to perform, as described below, the task of accurately defining this phenomenon and identifying bot accounts remains a challenge. At Texifter, we have endeavoured to bring nuance to this issue through a research project which explores the presence of automated accounts on Twitter. Initially, this project concerned itself with an attempt to identify bots which participated in online conversations around the prevailing cryptocurrency phenomenon. This article is the first in a series of three blog posts produced by the researchers at Texifter that outlines the contemporary phenomenon of Twitter bots….

Bots in their current iteration have a relatively short, albeit rapidly evolving history. Initially constructed with non-malicious intentions, it wasn’t until the late 1990s with the advent of Web 2.0 when bots began to develop a more negative reputation. Although bots have been used maliciously in denial-of-service (DDoS) attacks, spam emails, and mass identity theft, their purpose is not explicitly to incite mayhem.

Before the most recent political events, bots existed in chat rooms, operated as automated customer service agents on websites, and were a mainstay on dating websites. This familiar form of the bot is known to the majority of the general population as a “chatbot” – for instance, CleverBot was and still is a popular platform to talk to an “AI”. Another prominent example was Microsoft’s failed Twitter Chatbot Tay which made headlines in 2016 when “her” vocabulary and conversation functions were manipulated by Twitter users until “she” espoused neo-nazi views when “she” was subsequently deleted.

Image: XKCD Comic #632.

A Twitter bot is an account controlled by an algorithm or script, which is typically hosted on a cloud platform such as Heroku. They are typically, though not exclusively, scripted to conduct repetitive tasks.  For example, there are bots that retweet content containing particular keywords, reply to new followers, and direct messages to new followers; although they can be used for more complex tasks such as participating in online conversations. Bot accounts make up between 9 and 15% of all active accounts on Twitter; however, it is predicted that they account for a much greater percentage of total Twitter traffic. Twitter bots are generally not created with malicious intent; they are frequently used for online chatting or for raising the professional profile of a corporation – but their ability to pervade our online experience and shape political discourse warrants heightened scrutiny….(More)”.

Data journalism and the ethics of publishing Twitter data


Matthew L. Williams at Data Driven Journalism: “Collecting and publishing data collected from social media sites such as Twitter are everyday practices for the data journalist. Recent findings from Cardiff University’s Social Data Science Lab question the practice of publishing Twitter content without seeking some form of informed consent from users beforehand. Researchers found that tweets collected around certain topics, such as those related to terrorism, political votes, changes in the law and health problems, create datasets that might contain sensitive content, such as extreme political opinion, grossly offensive comments, overly personal revelations and threats to life (both to oneself and to others). Handling these data in the process of analysis (such as classifying content as hateful and potentially illegal) and reporting has brought the ethics of using social media in social research and journalism into sharp focus.

Ethics is an issue that is becoming increasingly salient in research and journalism using social media data. The digital revolution has outpaced parallel developments in research governance and agreed good practice. Codes of ethical conduct that were written in the mid twentieth century are being relied upon to guide the collection, analysis and representation of digital data in the twenty-first century. Social media is particularly ethically challenging because of the open availability of the data (particularly from Twitter). Many platforms’ terms of service specifically state users’ data that are public will be made available to third parties, and by accepting these terms users legally consent to this. However, researchers and data journalists must interpret and engage with these commercially motivated terms of service through a more reflexive lens, which implies a context sensitive approach, rather than focusing on the legally permissible uses of these data.

Social media researchers and data journalists have experimented with data from a range of sources, including Facebook, YouTube, Flickr, Tumblr and Twitter to name a few. Twitter is by far the most studied of all these networks. This is because Twitter differs from other networks, such as Facebook, that are organised around groups of ‘friends’, in that it is more ‘open’ and the data (in part) are freely available to researchers. This makes Twitter a more public digital space that promotes the free exchange of opinions and ideas. Twitter has become the primary space for online citizens to publicly express their reaction to events of national significance, and also the primary source of data for social science research into digital publics.

The Twitter streaming API provides three levels of data access: the free random 1% that provides ~5M tweets daily and the random 10% and 100% (chargeable or free to academic researchers upon request). Datasets on social interactions of this scale, speed and ease of access have been hitherto unrealisable in the social sciences and journalism, and have led to a flood of journal articles and news pieces, many of which include tweets with full text content and author identity without informed consent. This is presumably because of Twitter’s ‘open’ nature, which leads to the assumption that ‘these are public data’ and using it does not require the rigor and scrutiny of an ethical oversight. Even when these data are scrutinised, journalists don’t need to be convinced by the ‘public data’ argument, due to the lack of a framework to evaluate the potential harms to users. The Social Data Science Lab takes a more ethically reflexive approach to the use of social media data in social research, and carefully considers users’ perceptions, online context and the role of algorithms in estimating potentially sensitive user characteristics.

recent Lab survey conducted into users’ perceptions of the use of their social media posts found the following:

  • 94% were aware that social media companies had Terms of Service
  • 65% had read the Terms of Service in whole or in part
  • 76% knew that when accepting Terms of Service they were giving permission for some of their information to be accessed by third parties
  • 80% agreed that if their social media information is used in a publication they would expect to be asked for consent
  • 90% agreed that if their tweets were used without their consent they should be anonymized…(More)”.