When the Big Lie Meets Big Data

Curated on Posted on by Stefaan Verhulst

Peter Bruce in Scientific America: “…The science of predictive modeling has come a long way since 2004. Statisticians now build “personality” models and tie them into other predictor variables. … One such model bears the acronym “OCEAN,” standing for the personality characteristics (and their opposites) of openness, conscientiousness, extroversion, agreeableness, and neuroticism. Using Big Data at the individual level, machine learning methods might classify a person as, for example, “closed, introverted, neurotic, not agreeable, and conscientious.”

Alexander Nix, CEO of Cambridge Analytica (owned by Trump’s chief donor, Rebekah Mercer), says he has thousands of data points on you, and every other voter: what you buy or borrow, where you live, what you subscribe to, what you post on social media, etc. At a recent Concordia Summit, using the example of gun rights, Nix described how messages will be crafted to appeal specifically to you, based on your personality profile. Are you highly neurotic and conscientious? Nix suggests the image of a sinister gloved hand reaching through a broken window.

In his presentation, Nix noted that the goal is to induce behavior, not communicate ideas. So where does truth fit in? Johan Ugander, Assistant Professor of Management Science at Stanford, suggests that, for Nix and Cambridge Analytica, it doesn’t. In counseling the hypothetical owner of a private beach how to keep people off his property, Nix eschews the merely factual “Private Beach” sign, advocating instead a lie: “Sharks sighted.” Ugander, in his critique, cautions all data scientists against “building tools for unscrupulous targeting.”

The warning is needed, but may be too late. What Nix described in his presentation involved carefully crafted messages aimed at his target personalities. His messages pulled subtly on various psychological strings to manipulate us, and they obeyed no boundary of truth, but they required humans to create them.  The next phase will be the gradual replacement of human “craftsmanship” with machine learning algorithms that can supply targeted voters with a steady stream of content (from whatever source, true or false) designed to elicit desired behavior. Cognizant of the Pandora’s box that data scientists have opened, the scholarly journal Big Data has issued a call for papers for a future issue devoted to “Computational Propaganda.”…(More)”

Facebook artificial intelligence spots suicidal users

Curated on Posted on by Stefaan Verhulst

Leo Kelion at BBC News: “Facebook has begun using artificial intelligence to identify members that may be at risk of killing themselves.

The social network has developed algorithms that spot warning signs in users’ posts and the comments their friends leave in response.

After confirmation by Facebook’s human review team, the company contacts those thought to be at risk of self-harm to suggest ways they can seek help.

A suicide helpline chief said the move was “not just helpful but critical”.

The tool is being tested only in the US at present.

It marks the first use of AI technology to review messages on the network since founder Mark Zuckerberg announced last month that he also hoped to use algorithms to identify posts by terrorists, among other concerning content.

Facebook also announced new ways to tackle suicidal behaviour on its Facebook Live broadcast tool and has partnered with several US mental health organisations to let vulnerable users contact them via its Messenger platform.

Pattern recognition

Facebook has offered advice to users thought to be at risk of suicide for years, but until now it had relied on other users to bring the matter to its attention by clicking on a post’s report button.

It has now developed pattern-recognition algorithms to recognise if someone is struggling, by training them with examples of the posts that have previously been flagged.

Talk of sadness and pain, for example, would be one signal.

Responses from friends with phrases such as “Are you OK?” or “I’m worried about you,” would be another.

Once a post has been identified, it is sent for rapid review to the network’s community operations team.

“We know that speed is critical when things are urgent,” Facebook product manager Vanessa Callison-Burch told the BBC.

The director of the US National Suicide Prevention Lifeline praised the effort, but said he hoped Facebook would eventually do more than give advice, by also contacting those that could help….

The latest effort to help Facebook Live users follows the death of a 14-year-old-girl in Miami, who livestreamed her suicide on the platform in January.

However, the company said it had already begun work on its new tools before the tragedy.

The goal is to help at-risk users while they are broadcasting, rather than wait until their completed video has been reviewed some time later….(More)”.

Fighting Illegal Fishing With Big Data

Curated on Posted on by Stefaan Verhulst

Emily Matchar in Smithsonian: “In many ways, the ocean is the Wild West. The distances are vast, the law enforcement agents few and far between, and the legal jurisdiction often unclear. In this environment, illegal activity flourishes. Illegal fishing is so common that experts estimate as much as a third of fish sold in the U.S. was fished illegally. This illegal fishing decimates the ocean’s already dwindling fish populations and gives rise to modern slavery, where fishermen are tricked onto vessels and forced to work, sometimes for years.

A new use of data technology aims to help curb these abuses by shining a light on the high seas. The technology uses ships’ satellite signals to detect instances of transshipment, when two vessels meet at sea to exchange cargo. As transshipment is a major way illegally caught fish makes it into the legal supply chain, tracking it could potentially help stop the practice.

“[Transshipment] really allows people to do something out of sight,” says David Kroodsma, the research program director at Global Fishing Watch, an online data platform launched by Google in partnership with the nonprofits Oceana and SkyTruth. “It’s something that obscures supply chains. It’s basically being able to do things without any oversight. And that’s a problem when you’re using a shared resource like the oceans.”

Global Fishing Watch analyzed some 21 billion satellite signals broadcast by ships, which are required to carry transceivers for collision avoidance, from between 2012 and 2016. It then used an artificial intelligence system it created to identify which ships were refrigerated cargo vessels (known in the industry as “reefers”). They then verified this information with fishery registries and other sources, eventually identifying 794 reefers—90 percent of the world’s total number of such vessels. They tracked instances where a reefer and a fishing vessel were moving at similar speeds in close proximity, labeling these instances as “likely transshipments,” and also traced instances where reefers were traveling in a way that indicated a rendezvous with a fishing vessel, even if no fishing vessel was present—fishing vessels often turn off their satellite systems when they don’t want to be seen. All in all there were more than 90,000 likely or potential transshipments recorded.

Even if these encounters were in fact transshipments, they would not all have been for nefarious purposes. They may have taken place to refuel or load up on supplies. But looking at the patterns of where the potential transshipments happen is revealing. Very few are seen close to the coasts of the U.S., Canada and much of Europe, all places with tight fishery regulations. There are hotspots off the coast of Peru and Argentina, all over Africa, and off the coast of Russia. Some 40 percent of encounters happen in international waters, far enough off the coast that no country has jurisdiction.

The tracked reefers were flying flags from some 40 different countries. But that doesn’t necessarily tell us much about where they really come from. Nearly half of the reefers tracked were flying “flags of convenience,” meaning they’re registered in countries other than where the ship’s owners are from to take advantage of those countries’ lax regulations….(More)”

Read more: http://www.smithsonianmag.com/innovation/fighting-illegal-fishing-big-data-180962321/#7eCwGrGS5v5gWjFz.99
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Will Democracy Survive Big Data and Artificial Intelligence?

Curated on Posted on by Stefaan Verhulst

Dirk Helbing, Bruno S. Frey, Gerd Gigerenzer, Ernst Hafen, Michael Hagner, Yvonne Hofstetter, Jeroen van den Hoven, Roberto V. Zicari, and Andrej Zwitter in Scientific American: “….In summary, it can be said that we are now at a crossroads (see Fig. 2). Big data, artificial intelligence, cybernetics and behavioral economics are shaping our society—for better or worse. If such widespread technologies are not compatible with our society’s core values, sooner or later they will cause extensive damage. They could lead to an automated society with totalitarian features. In the worst case, a centralized artificial intelligence would control what we know, what we think and how we act. We are at the historic moment, where we have to decide on the right path—a path that allows us all to benefit from the digital revolution. Therefore, we urge to adhere to the following fundamental principles:

1. to increasingly decentralize the function of information systems;

2. to support informational self-determination and participation;

3. to improve transparency in order to achieve greater trust;

4. to reduce the distortion and pollution of information;

5. to enable user-controlled information filters;

6. to support social and economic diversity;

7. to improve interoperability and collaborative opportunities;

8. to create digital assistants and coordination tools;

9. to support collective intelligence, and

10. to promote responsible behavior of citizens in the digital world through digital literacy and enlightenment.

Following this digital agenda we would all benefit from the fruits of the digital revolution: the economy, government and citizens alike. What are we waiting for?A strategy for the digital age

Big data and artificial intelligence are undoubtedly important innovations. They have an enormous potential to catalyze economic value and social progress, from personalized healthcare to sustainable cities. It is totally unacceptable, however, to use these technologies to incapacitate the citizen. Big nudging and citizen scores abuse centrally collected personal data for behavioral control in ways that are totalitarian in nature. This is not only incompatible with human rights and democratic principles, but also inappropriate to manage modern, innovative societies. In order to solve the genuine problems of the world, far better approaches in the fields of information and risk management are required. The research area of responsible innovation and the initiative ”Data for Humanity” (see “Big Data for the benefit of society and humanity”) provide guidance as to how big data and artificial intelligence should be used for the benefit of society….(More)”

Data Disrupts Corruption

Curated on Posted on by Stefaan Verhulst

Carlos Santiso & Ben Roseth at Stanford Social Innovation Review: “…The Panama Papers scandal demonstrates the power of data analytics to uncover corruption in a world flooded with terabytes needing only the computing capacity to make sense of it all. The Rousse impeachment illustrates how open data can be used to bring leaders to account. Together, these stories show how data, both “big” and “open,” is driving the fight against corruption with fast-paced, evidence-driven, crowd-sourced efforts. Open data can put vast quantities of information into the hands of countless watchdogs and whistleblowers. Big data can turn that information into insight, making corruption easier to identify, trace, and predict. To realize the movement’s full potential, technologists, activists, officials, and citizens must redouble their efforts to integrate data analytics into policy making and government institutions….

Making big data open cannot, in itself, drive anticorruption efforts. “Without analytics,” a 2014 White House report on big data and individual privacy underscored, “big datasets could be stored, and they could be retrieved, wholly or selectively. But what comes out would be exactly what went in.”

In this context, it is useful to distinguish the four main stages of data analytics to illustrate its potential in the global fight against corruption: Descriptive analytics uses data to describe what has happened in analyzing complex policy issues; diagnostic analytics goes a step further by mining and triangulating data to explain why a specific policy problem has happened, identify its root causes, and decipher underlying structural trends; predictive analytics uses data and algorithms to predict what is most likely to occur, by utilizing machine learning; and prescriptive analytics proposes what should be done to cause or prevent something from happening….

Despite the big data movement’s promise for fighting corruption, many challenges remain. The smart use of open and big data should focus not only on uncovering corruption, but also on better understanding its underlying causes and preventing its recurrence. Anticorruption analytics cannot exist in a vacuum; it must fit in a strategic institutional framework that starts with quality information and leads to reform. Even the most sophisticated technologies and data innovations cannot prevent what French novelist Théophile Gautier described as the “inexplicable attraction of corruption, even amongst the most honest souls.” Unless it is harnessed for improvements in governance and institutions, data analytics will not have the impact that it could, nor be sustainable in the long run…(More)”.

A solution to the single-question crowd wisdom problem

Curated on Posted on by Stefaan Verhulst

Dražen Prelec,H. Sebastian Seung & John McCoy in Nature: “Once considered provocative, the notion that the wisdom of the crowd is superior to any individual has become itself a piece of crowd wisdom, leading to speculation that online voting may soon put credentialed experts out of business. Recent applications include political and economic forecasting, evaluating nuclear safety, public policy, the quality of chemical probes, and possible responses to a restless volcano. Algorithms for extracting wisdom from the crowd are typically based on a democratic voting procedure. They are simple to apply and preserve the independence of personal judgment. However, democratic methods have serious limitations. They are biased for shallow, lowest common denominator information, at the expense of novel or specialized knowledge that is not widely shared. Adjustments based on measuring confidence do not solve this problem reliably. Here we propose the following alternative to a democratic vote: select the answer that is more popular than people predict. We show that this principle yields the best answer under reasonable assumptions about voter behaviour, while the standard ‘most popular’ or ‘most confident’ principles fail under exactly those same assumptions. Like traditional voting, the principle accepts unique problems, such as panel decisions about scientific or artistic merit, and legal or historical disputes. The potential application domain is thus broader than that covered by machine learning and psychometric methods, which require data across multiple questions…(More).

Human Decisions and Machine Predictions

Curated on Posted on by Stefaan Verhulst

NBER Working Paper by Jon Kleinberg, Himabindu Lakkaraju, Jure Leskovec, Jens Ludwig, and Sendhil Mullainatha: “We examine how machine learning can be used to improve and understand human decision-making. In particular, we focus on a decision that has important policy consequences. Millions of times each year, judges must decide where defendants will await trial—at home or in jail. By law, this decision hinges on the judge’s prediction of what the defendant would do if released. This is a promising machine learning application because it is a concrete prediction task for which there is a large volume of data available. Yet comparing the algorithm to the judge proves complicated. First, the data are themselves generated by prior judge decisions. We only observe crime outcomes for released defendants, not for those judges detained. This makes it hard to evaluate counterfactual decision rules based on algorithmic predictions. Second, judges may have a broader set of preferences than the single variable that the algorithm focuses on; for instance, judges may care about racial inequities or about specific crimes (such as violent crimes) rather than just overall crime risk. We deal with these problems using different econometric strategies, such as quasi-random assignment of cases to judges. Even accounting for these concerns, our results suggest potentially large welfare gains: a policy simulation shows crime can be reduced by up to 24.8% with no change in jailing rates, or jail populations can be reduced by 42.0% with no increase in crime rates. Moreover, we see reductions in all categories of crime, including violent ones. Importantly, such gains can be had while also significantly reducing the percentage of African-Americans and Hispanics in jail. We find similar results in a national dataset as well. In addition, by focusing the algorithm on predicting judges’ decisions, rather than defendant behavior, we gain some insight into decision-making: a key problem appears to be that judges to respond to ‘noise’ as if it were signal. These results suggest that while machine learning can be valuable, realizing this value requires integrating these tools into an economic framework: being clear about the link between predictions and decisions; specifying the scope of payoff functions; and constructing unbiased decision counterfactuals….(More)”

Understanding Actionable Intelligence for Social Policy

Curated on Posted on by Stefaan Verhulst

Video on “The Actionable Intelligence (AI) model is a new approach to policy development. The AI approach is supported by Integrated Data Systems (IDS) which link administrative records from multiple agencies to give a broader view of social problems and policy solutions. The use of linked administrative data allows policy analysts, program evaluators and social innovators to test new social program ideas at a much lower cost and higher speed. AI uses these IDS to create a newly informed dialogue among executive leaders, stakeholders and researchers regarding what works best, for whom and in the most cost effective way….(More videos from AISP-UPENN)

Understanding Actionable Intelligence for Social Policy from AISP_UPENN on Vimeo.

Best Government Emerging Technologies

Curated on Posted on by Stefaan Verhulst

Report released at the World Government Summit (Dubai): “… the “Best Government Emerging Technologies” recognises governments that are experimenting with emerging technologies to provide government services more e ciently, e ectively and have proven results showing how they have created greater public value and transformed people›s lives.

For this purpose, the Prime Minister’s Office has joined forces with Indra to analyse and identify 29 Emerging Technologies, grouped in 9 categories that include technologies such as Artificial Intelligence, Blockchain, Cloud Computing, Robotics & Space, Smart Platforms, amongst other.

Wherever possible, case studies have been analysed as example of the use of the technology in public bodies and government, taking into account that some of these technologies may not have been implemented yet in the public sector and therefore have not a ected the lives of citizens. e analysis comprises 73 international case studies from 32 di erent countries.

is document represents an executive summary of the analysis ndings, incorporating a brief description of the main Emerging Technologies where the selected cutting-edge digital technologies are introduced, followed by a number of examples of international case studies in which governments and public bodies have implemented these technologies….

Screen Shot 2017-02-13 at 4.35.40 PM(More)

Code-Dependent: Pros and Cons of the Algorithm Age

Curated on Posted on by Stefaan Verhulst

 and  at PewResearch Center: “Algorithms are instructions for solving a problem or completing a task. Recipes are algorithms, as are math equations. Computer code is algorithmic. The internet runs on algorithms and all online searching is accomplished through them. Email knows where to go thanks to algorithms. Smartphone apps are nothing but algorithms. Computer and video games are algorithmic storytelling. Online dating and book-recommendation and travel websites would not function without algorithms. GPS mapping systems get people from point A to point B via algorithms. Artificial intelligence (AI) is naught but algorithms. The material people see on social media is brought to them by algorithms. In fact, everything people see and do on the web is a product of algorithms. Every time someone sorts a column in a spreadsheet, algorithms are at play, and most financial transactions today are accomplished by algorithms. Algorithms help gadgets respond to voice commands, recognize faces, sort photos and build and drive cars. Hacking, cyberattacks and cryptographic code-breaking exploit algorithms. Self-learning and self-programming algorithms are now emerging, so it is possible that in the future algorithms will write many if not most algorithms.

Algorithms are often elegant and incredibly useful tools used to accomplish tasks. They are mostly invisible aids, augmenting human lives in increasingly incredible ways. However, sometimes the application of algorithms created with good intentions leads to unintended consequences. Recent news items tie to these concerns: