MIT map offers real-time, crowd-sourced flood reporting during Hurricane Irma

Curated on Posted on by Stefaan Verhulst

MIT News: “As Hurricane Irma bears down on the U.S., the MIT Urban Risk Lab has launched a free, open-source platform that will help residents and government officials track flooding in Broward County, Florida. The platform, RiskMap.us, is being piloted to enable both residents and emergency managers to obtain better information on flooding conditions in near-real time.

Residents affected by flooding can add information to the publicly available map via popular social media channels. Using Twitter, Facebook, and Telegram, users submit reports by sending a direct message to the Risk Map chatbot. The chatbot replies to users with a one-time link through which they can upload information including location, flood depth, a photo, and description.

Residents and government officials can view the map to see recent flood reports to understand changing flood conditions across the county. Tomas Holderness, a research scientist in the MIT Department of Architecture, led the design of the system. “This project shows the importance that citizen data has to play in emergencies,” he says. “By connecting residents and emergency managers via social messaging, our map helps keep people informed and improve response times.”…

The Urban Risk Lab also piloted the system in Indonesia — where the project is called PetaBencana.id, or “Map Disaster” — during a large flood event on Feb. 20, 2017.

During the flooding, over 300,000 users visited the public website in 24 hours, and the map was integrated into the Uber application to help drivers avoid flood waters. The project in Indonesia is supported by a grant from USAID and is working in collaboration with the Indonesian Federal Emergency Management Agency, the Pacific Disaster Centre, and the Humanitarian Open Street Map Team.

The Urban Risk Lab team is also working in India on RiskMap.in….(More)”.

Feeding the Machine: Policing, Crime Data, & Algorithms

Curated on Posted on by Stefaan Verhulst

Elizabeth E. Joh at William & Mary Bill of Rights J. (2017 Forthcoming): “Discussions of predictive algorithms used by the police tend to assume the police are merely end users of big data. Accordingly, police departments are consumers and clients of big data — not much different than users of Spotify, Netflix, Amazon, or Facebook. Yet this assumption about big data policing contains a flaw. Police are not simply end users of big data. They generate the information that big data programs rely upon. This essay explains why predictive policing programs can’t be fully understood without an acknowledgment of the role police have in creating its inputs. Their choices, priorities, and even omissions become the inputs algorithms use to forecast crime. The filtered nature of crime data matters because these programs promise cutting edge results, but may deliver analyses with hidden limitations….(More)”.

The Use of Big Data Analytics by the IRS: Efficient Solutions or the End of Privacy as We Know It?

Curated on Posted on by Stefaan Verhulst

Kimberly A. Houser and Debra Sanders in the Vanderbilt Journal of Entertainment and Technology Law: “This Article examines the privacy issues resulting from the IRS’s big data analytics program as well as the potential violations of federal law. Although historically, the IRS chose tax returns to audit based on internal mathematical mistakes or mismatches with third party reports (such as W-2s), the IRS is now engaging in data mining of public and commercial data pools (including social media) and creating highly detailed profiles of taxpayers upon which to run data analytics. This Article argues that current IRS practices, mostly unknown to the general public are violating fair information practices. This lack of transparency and accountability not only violates federal law regarding the government’s data collection activities and use of predictive algorithms, but may also result in discrimination. While the potential efficiencies that big data analytics provides may appear to be a panacea for the IRS’s budget woes, unchecked, these activities are a significant threat to privacy. Other concerns regarding the IRS’s entrée into big data are raised including the potential for political targeting, data breaches, and the misuse of such information. This Article intends to bring attention to these privacy concerns and contribute to the academic and policy discussions about the risks presented by the IRS’s data collection, mining and analytics activities….(More)”.

How to Regulate Artificial Intelligence

Curated on Posted on by Stefaan Verhulst

Oren Etzioni in the New York Times: “…we should regulate the tangible impact of A.I. systems (for example, the safety of autonomous vehicles) rather than trying to define and rein in the amorphous and rapidly developing field of A.I.

I propose three rules for artificial intelligence systems that are inspired by, yet develop further, the “three laws of robotics” that the writer Isaac Asimov introduced in 1942: A robot may not injure a human being or, through inaction, allow a human being to come to harm; a robot must obey the orders given it by human beings, except when such orders would conflict with the previous law; and a robot must protect its own existence as long as such protection does not conflict with the previous two laws.

These three laws are elegant but ambiguous: What, exactly, constitutes harm when it comes to A.I.? I suggest a more concrete basis for avoiding A.I. harm, based on three rules of my own.

First, an A.I. system must be subject to the full gamut of laws that apply to its human operator. This rule would cover private, corporate and government systems. We don’t want A.I. to engage in cyberbullying, stock manipulation or terrorist threats; we don’t want the F.B.I. to release A.I. systems that entrap people into committing crimes. We don’t want autonomous vehicles that drive through red lights, or worse, A.I. weapons that violate international treaties.

Our common law should be amended so that we can’t claim that our A.I. system did something that we couldn’t understand or anticipate. Simply put, “My A.I. did it” should not excuse illegal behavior.

My second rule is that an A.I. system must clearly disclose that it is not human. As we have seen in the case of bots — computer programs that can engage in increasingly sophisticated dialogue with real people — society needs assurances that A.I. systems are clearly labeled as such. In 2016, a bot known as Jill Watson, which served as a teaching assistant for an online course at Georgia Tech, fooled students into thinking it was human. A more serious example is the widespread use of pro-Trump political bots on social media in the days leading up to the 2016 elections, according to researchers at Oxford….

My third rule is that an A.I. system cannot retain or disclose confidential information without explicit approval from the source of that information…(More)”

Crowdsourcing website is helping volunteers save lives in hurricane-hit Houston

Curated on Posted on by Stefaan Verhulst

Ana Campoy at Quartz: “On the night of Sunday, Aug. 28, Matthew Marchetti was one of thousands of Houstonians feeling powerless as their city drowned in tropical storm Harvey’s deluge.

By Monday morning, the 27-year-old developer, sitting in his leaky office, had slapped together an online mapping tool to track stranded residents. A day later, nearly 5,000 people had registered to be rescued, and 2,700 of them were safe.

If there’s a silver lining to Harvey, it’s the flood of civilian volunteers such as Marchetti who have joined the rescue effort. It became pretty clear shortly after the storm started pounding Houston that the city would need their help. The heavy rains quickly outstripped authorities’ ability to respond. People watched water levels rise around them while they waited on hold to get connected to a 911 dispatcher. Desperate local officials asked owners of high-water vehicles and boats to help collect their fellow citizens trapped on second-stories and roofs.

In the past, disaster volunteers have relied on social media and Zello, an app that turns your phone into a walkie-talkie, to organize. … Harvey’s magnitude, both in terms of damage and the number of people anxious to pitch in, also overwhelmed those grassroots organizing methods, says Marchetti, who spent the spent the first days after the storm hit monitoring Facebook and Zello to figure out what was needed where.

“The channels were just getting overloaded with people asking ‘Where do I go?’” he says. “We’ve tried to cut down on the level of noise.”

The idea behind his project, Houstonharveyrescue.com, is simple. The map lets people in need register their location. They are asked to include details—for example, if they’re sick or have small children—and their cell phone numbers.

The army of rescuers, who can also register on the site, can then easily spot the neediest cases. A team of 100 phone dispatchers follows up with those wanting to be rescued, and can send mass text messages with important information. An algorithm weeds out any repeats.

It might be one of the first open-sourced rescue missions in the US, and could be a valuable blueprint for future disaster volunteers. (For a similar civilian-led effort outside the US, look at Tijuana’s Strategic Committee for Humanitarian Aid, a Facebook group that sprouted last year when the Mexican border city was overwhelmed by a wave of Haitian immigrants.)…(More)”.

Who Falls for Fake News? The Roles of Analytic Thinking, Motivated Reasoning, Political Ideology, and Bullshit Receptivity

Curated on Posted on by Stefaan Verhulst

Paper by Gordon Pennycook and David G. Rand: “Inaccurate beliefs pose a threat to democracy and fake news represents a particularly egregious and direct avenue by which inaccurate beliefs have been propagated via social media. Here we investigate the cognitive psychological profile of individuals who fall prey to fake news. We find a consistent positive correlation between the propensity to think analytically – as measured by the Cognitive Reflection Test (CRT) – and the ability to differentiate fake news from real news (“media truth discernment”). This was true regardless of whether the article’s source was indicated (which, surprisingly, also had no main effect on accuracy judgments). Contrary to the motivated reasoning account, CRT was just as positively correlated with media truth discernment, if not more so, for headlines that aligned with individuals’ political ideology relative to those that were politically discordant. The link between analytic thinking and media truth discernment was driven both by a negative correlation between CRT and perceptions of fake news accuracy (particularly among Hillary Clinton supporters), and a positive correlation between CRT and perceptions of real news accuracy (particularly among Donald Trump supporters). This suggests that factors that undermine the legitimacy of traditional news media may exacerbate the problem of inaccurate political beliefs among Trump supporters, who engaged in less analytic thinking and were overall less able to discern fake from real news (regardless of the news’ political valence). We also found consistent evidence that pseudo-profound bullshit receptivity negatively correlates with perceptions of fake news accuracy; a correlation that is mediated by analytic thinking. Finally, analytic thinking was associated with an unwillingness to share both fake and real news on social media. Our results indicate that the propensity to think analytically plays an important role in the recognition of misinformation, regardless of political valence – a finding that opens up potential avenues for fighting fake news….(More)”.

From Katrina To Harvey: How Disaster Relief Is Evolving With Technology

Curated on Posted on by Stefaan Verhulst

Cale Guthrie Weissman at Fast Company: “Open data may sound like a nerdy thing, but this weekend has proven it’s also a lifesaver in more ways than one.

As Hurricane Harvey pelted the southern coast of Texas, a local open-data resource helped provide accurate and up-to-date information to the state’s residents. Inside Harris County’s intricate bayou system–intended to both collect water and effectively drain it–gauges were installed to sense when water is overflowing. The sensors transmit the data to a website, which has become a vital go-to for Houston residents….

This open access to flood gauges is just one of the many ways new tech-driven projects have helped improve responses to disasters over the years. “There’s no question that technology has played a much more significant role,” says Lemaitre, “since even Hurricane Sandy.”

While Sandy was noted in 2012 for its ability to connect people with Twitter hashtags and other relatively nascent social apps like Instagram, the last few years have brought a paradigm shift in terms of how emergency relief organizations integrate technology into their responses….

Social media isn’t just for the residents. Local and national agencies–including FEMA–rely on this information and are using it to help create faster and more effective disaster responses. Following the disaster with Hurricane Katrina, FEMA worked over the last decade to revamp its culture and methods for reacting to these sorts of situations. “You’re seeing the federal government adapt pretty quickly,” says Lemaitre.

There are a few examples of this. For instance, FEMA now has an app to push necessary information about disaster preparedness. The agency also employs people to cull the open web for information that would help make its efforts better and more effective. These “social listeners” look at all the available Facebook, Snapchat, and other social media posts in aggregate. Crews are brought on during disasters to gather intelligence, and then report about areas that need relief efforts–getting “the right information to the right people,” says Lemaitre.

There’s also been a change in how this information is used. Often, when disasters are predicted, people send supplies to the affected areas as a way to try and help out. Yet they don’t know exactly where they should send it, and local organizations sometimes become inundated. This creates a huge logistical nightmare for relief organizations that are sitting on thousands of blankets and tarps in one place when they should be actively dispersing them across hundreds of miles.

“Before, you would just have a deluge of things dropped on top of a disaster that weren’t particularly helpful at times,” says Lemaitre. Now people are using sites like Facebook to ask where they should direct the supplies. For example, after a bad flood in Louisiana last year, a woman announced she had food and other necessities on Facebook and was able to direct the supplies to an area in need. This, says Lemaitre, is “the most effective way.”

Put together, Lemaitre has seen agencies evolve with technology to help create better systems for quicker disaster relief. This has also created a culture of learning updates and reacting in real time. Meanwhile, more data is becoming open, which is helping both people and agencies alike. (The National Weather Service, which has long trumpeted its open data for all, has become a revered stalwart for such information, and has already proven indispensable in Houston.)

Most important, the pace of technology has caused organizations to change their own procedures. Twelve years ago, during Katrina, the protocol was to wait until an assessment before deploying any assistance. Now organizations like FEMA know that just doesn’t work. “You can’t afford to lose time,” says Lemaitre. “Deploy as much as you can and be fast about it–you can always scale back.”

It’s important to note that, even with rapid technological improvements, there’s no way to compare one disaster response to another–it’s simply not apples to apples. All the same, organizations are still learning about where they should be looking and how to react, connecting people to their local communities when they need them most….(More)”.

From ‘Opening Up’ to Democratic Renewal: Deepening Public Engagement in Legislative Committees

Curated on Posted on by Stefaan Verhulst

Carolyn M. Hendriks and Adrian Kay in Government and Opposition: “Many legislatures around the world are undergoing a ‘participatory makeover’. Parliaments are hosting open days and communicating the latest parliamentary updates via websites and social media. Public activities such as these may make parliaments more informative and accessible, but much more could be done to foster meaningful democratic renewal. In particular, participatory efforts ought to be engaging citizens in a central task of legislatures – to deliberate and make decisions on collective issues. In this article, the potential of parliamentary committees to bring the public closer to legislative deliberations is considered. Drawing on insights from the practice and theory of deliberative democracy, the article discusses why and how deeper and more inclusive forms of public engagement can strengthen the epistemic, representative and deliberative capacities of parliamentary committees. Practical examples are considered to illustrate the possibilities and challenges of broadening public involvement in committee work….(More)”

Crowdsourcing the Charlottesville Investigation

Curated on Posted on by Stefaan Verhulst

Internet sleuths got to work, and by Monday morning they were naming names and calling for arrests.

The name of the helmeted man went viral after New York Daily News columnist Shaun King posted a series of photos on Twitter and Facebook that more clearly showed his face and connected him to photos from a Facebook account. “Neck moles gave it away,” King wrote in his posts, which were shared more than 77,000 times. But the name of the red-bearded assailant was less clear: some on Twitter claimed it was a Texas man who goes by a Nordic alias online. Others were sure it was a Michigan man who, according to Facebook, attended high school with other white nationalist demonstrators depicted in photos from Charlottesville.

After being contacted for comment by The Marshall Project, the Michigan man removed his Facebook page from public view.

Such speculation, especially when it is not conclusive, has created new challenges for law enforcement. There is the obvious risk of false identification. In 2013, internet users wrongly identified university student Sunil Tripathi as a suspect in the Boston marathon bombing, prompting the internet forum Reddit to issue an apology for fostering “online witch hunts.” Already, an Arkansas professor was misidentified as as a torch-bearing protester, though not a criminal suspect, at the Charlottesville rallies.

Beyond the cost to misidentified suspects, the crowdsourced identification of criminal suspects is both a benefit and burden to investigators.

“If someone says: ‘hey, I have a picture of someone assaulting another person, and committing a hate crime,’ that’s great,” said Sgt. Sean Whitcomb, the spokesman for the Seattle Police Department, which used social media to help identify the pilot of a drone that crashed into a 2015 Pride Parade. (The man was convicted in January.) “But saying, ‘I am pretty sure that this person is so and so’. Well, ‘pretty sure’ is not going to cut it.”

Still, credible information can help police establish probable cause, which means they can ask a judge to sign off on either a search warrant, an arrest warrant, or both….(More)“.

Inside the Lab That’s Quantifying Happiness

Curated on Posted on by Stefaan Verhulst

Rowan Jacobsen at Outside: “In Mississippi, people tweet about cake and cookies an awful lot; in Colorado, it’s noodles. In Mississippi, the most-tweeted activity is eating; in Colorado, it’s running, skiing, hiking, snowboarding, and biking, in that order. In other words, the two states fall on opposite ends of the behavior spectrum. If you were to assign a caloric value to every food mentioned in every tweet by the citizens of the United States and a calories-burned value to every activity, and then totaled them up, you would find that Colorado tweets the best caloric ratio in the country and Mississippi the worst.

Sure, you’d be forgiven for doubting people’s honesty on Twitter. On those rare occasions when I destroy an entire pint of Ben and Jerry’s, I most assuredly do not tweet about it. Likewise, I don’t reach for my phone every time I strap on a pair of skis.

And yet there’s this: Mississippi has the worst rate of diabetes and heart disease in the country and Colorado has the best. Mississippi has the second-highest percentage of obesity; Colorado has the lowest. Mississippi has the worst life expectancy in the country; Colorado is near the top. Perhaps we are being more honest on social media than we think. And perhaps social media has more to tell us about the state of the country than we realize.

That’s the proposition of Peter Dodds and Chris Danforth, who co-direct the University of Vermont’s Computational Story Lab, a warren of whiteboards and grad students in a handsome brick building near the shores of Lake Champlain. Dodds and Danforth are applied mathematicians, but they would make a pretty good comedy duo. When I stopped by the lab recently, both were in running clothes and cracking jokes. They have an abundance of curls between them and the wiry energy of chronic thinkers. They came to UVM in 2006 to start the Vermont Complex Systems Center, which crunches big numbers from big systems and looks for patterns. Out of that, they hatched the Computational Story Lab, which sifts through some of that public data to discern the stories we’re telling ourselves. “It took us a while to come up with the name,” Dodds told me as we shotgunned espresso and gazed into his MacBook. “We were going to be the Department of Recreational Truth.”

This year, they teamed up with their PhD student Andy Reagan to launch the Lexicocalorimeter, an online tool that uses tweets to compute the calories in and calories out for every state. It’s no mere party trick; the Story Labbers believe the Lexicocalorimeter has important advantages over slower, more traditional methods of gathering health data….(More)”.