DATA – Page 395 – The Living Library

Automation Beyond the Physical: AI in the Public Sector

Curated on September 6, 2017August 3, 2018 by Stefaan Verhulst

Ben Miller at Government Technology: “…The technology is, by nature, broadly applicable. If a thing involves data — “data” itself being a nebulous word — then it probably has room for AI. AI can help manage the data, analyze it and find patterns that humans might not have thought of. When it comes to big data, or data sets so big that they become difficult for humans to manually interact with, AI leverages the speedy nature of computing to find relationships that might otherwise be proverbial haystack needles.

One early area of government application is in customer service chatbots. As state and local governments started putting information on websites in the past couple of decades, they found that they could use those portals as a means of answering questions that constituents used to have to call an office to ask.

Ideally that results in a cyclical victory: Government offices didn’t have as many calls to answer, so they could devote more time and resources to other functions. And when somebody did call in, their call might be answered faster.

With chatbots, governments are betting they can answer even more of those questions. When he was the chief technology and innovation officer of North Carolina, Eric Ellis oversaw the setup of a system that did just that for IT help desk calls.

Turned out, more than 80 percent of the help desk’s calls were people who wanted to change their passwords. For something like that, where the process is largely the same each time, a bot can speed up the process with a little help from AI. Then, just like with the government Web portal, workers are freed up to respond to the more complicated calls faster….

Others are using AI to recognize and report objects in photographs and videos — guns, waterfowl, cracked concrete, pedestrians, semi-trucks, everything. Others are using AI to help translate between languages dynamically. Some want to use it to analyze the tone of emails. Some are using it to try to keep up with cybersecurity threats even as they morph and evolve. After all, if AI can learn to beat professional poker players, then why can’t it learn how digital black hats operate?

Castro sees another use for the technology, a more introspective one. The problem is this: The government workforce is a lot older than the private sector, and that can make it hard to create culture change. According to U.S. Census Bureau data, about 27 percent of public-sector workers are millennials, compared with 38 percent in the private sector.

“The traditional view [of government work] is you fill out a lot of forms, there are a lot of boring meetings. There’s a lot of bureaucracy in government,” Castro said. “AI has the opportunity to change a lot of that, things like filling out forms … going to routine meetings and stuff.”

As AI becomes more and more ubiquitous, people who work both inside and with government are coming up with an ever-expanding list of ways to use it. Here’s an inexhaustive list of specific use cases — some of which are already up and running and some of which are still just ideas….(More)”.

How to Regulate Artificial Intelligence

Curated on September 3, 2017August 3, 2018 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 September 1, 2017July 19, 2019 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.

The good news is that many people stepped up; the bad news, that it’s difficult—and risky—to rapidly coordinate and deploy the overflow of good intentions from a disparate group of non-professional rescue workers.

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)”.

Debating big data: A literature review on realizing value from big data

Curated on August 30, 2017August 3, 2018 by Stefaan Verhulst

Wendy Arianne Günther et al in The Journal of Strategic Information Systems: “Big data has been considered to be a breakthrough technological development over recent years. Notwithstanding, we have as yet limited understanding of how organizations translate its potential into actual social and economic value. We conduct an in-depth systematic review of IS literature on the topic and identify six debates central to how organizations realize value from big data, at different levels of analysis. Based on this review, we identify two socio-technical features of big data that influence value realization: portability and interconnectivity. We argue that, in practice, organizations need to continuously realign work practices, organizational models, and stakeholder interests in order to reap the benefits from big data. We synthesize the findings by means of an integrated model….(More)”.

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

Curated on August 30, 2017August 3, 2018 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 August 29, 2017August 3, 2018 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)”.

What does it mean to be differentially private?

Curated on August 17, 2017August 3, 2018 by Stefaan Verhulst

Paul Francis at IAPP: “Back in June 2016, Apple announced it will use differential privacy to protect individual privacy for certain data that it collects. Though already a hot research topic for over a decade, this announcement introduced differential privacy to the broader public. Before that announcement, Google had already been using differential privacy for collecting Chrome usage statistics. And within the last month, Uber announced that they too are using differential privacy.

If you’ve done a little homework on differential privacy, you may have learned that it provides provable guarantees of privacy and concluded that a database that is differentially private is, well, private — in other words, that it protects individual privacy. But that isn’t necessarily the case. When someone says, “a database is differentially private,” they don’t mean that the database is private. Rather, they mean, “the privacy of the database can be measured.”

Really, it is like saying that “a bridge is weight limited.” If you know the weight limit of a bridge, then yes, you can use the bridge safely. But the bridge isn’t safe under all conditions. You can exceed the weight limit and hurt yourself.

The weight limit of bridges is expressed in tons, kilograms or number of people. Simplifying here a bit, the amount of privacy afforded by a differentially private database is expressed as a number, by convention labeled ε (epsilon). Lower ε means more private.

All bridges have a weight limit. Everybody knows this, so it sounds dumb to say, “a bridge is weight limited.” And guess what? All databases are differentially private. Or, more precisely, all databases have an ε. A database with no privacy protections at all has an ε of infinity. It is pretty misleading to call such a database differentially private, but mathematically speaking, it is not incorrect to do so. A database that can’t be queried at all has an ε of zero. Private, but useless.

In their paper on differential privacy for statistics, Cynthia Dwork and Adam Smith write, “The choice of ε is essentially a social question. We tend to think of ε as, say, 0.01, 0.1, or in some cases, ln 2 or ln 3.” The natural logarithm of 3 (ln 3) is around 1.1….(More)”.

Crowdsourcing the Charlottesville Investigation

Curated on August 16, 2017August 3, 2018 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)“.

Can AI tools replace feds?

Curated on August 16, 2017August 3, 2018 by Stefaan Verhulst

Derek B. Johnson at FCW: “The Heritage Foundation…is calling for increased reliance on automation and the potential creation of a “contractor cloud” offering streamlined access to private sector labor as part of its broader strategy for reorganizing the federal government.

Seeking to take advantage of a united Republican government and a president who has vowed to reform the civil service, the foundation drafted a pair of reports this year attempting to identify strategies for consolidating, merging or eliminating various federal agencies, programs and functions. Among those strategies is a proposal for the Office of Management and Budget to issue a report “examining existing government tasks performed by generously-paid government employees that could be automated.”

Citing research on the potential impacts of automation on the United Kingdom’s civil service, the foundation’s authors estimated that similar efforts across the U.S. government could yield $23.9 billion in reduced personnel costs and a reduction in the size of the federal workforce by 288,000….

The Heritage report also called on the federal government to consider a “contracting cloud.” The idea would essentially be for a government version of TaskRabbit, where agencies could select from a pool of pre-approved individual contractors from the private sector who could be brought in for specialized or seasonal work without going through established contracts. Greszler said the idea came from speaking with subcontractors who complained about having to kick over a certain percentage of their payments to prime contractors even as they did all the work.

Right now the foundation is only calling for the government to examine the potential of the issue and how it would interact with existing or similar vehicles for contracting services like the GSA schedule. Greszler emphasized that any pool of workers would need to be properly vetted to ensure they met federal standards and practices.

“There has to be guidelines or some type of checks, so you’re not having people come off the street and getting access to secure government data,” she said….(More)