Artificial Intelligence can streamline public comment for federal agencies

Curated on Posted on by Stefaan Verhulst

John Davis at the Hill: “…What became immediately clear to me was that — although not impossible to overcome — the lack of consistency and shared best practices across all federal agencies in accepting and reviewing public comments was a serious impediment. The promise of Natural Language Processing and cognitive computing to make the public comment process light years faster and more transparent becomes that much more difficult without a consensus among federal agencies on what type of data is collected – and how.

“There is a whole bunch of work we have to do around getting government to be more customer friendly and making it at least as easy to file your taxes as it is to order a pizza or buy an airline ticket,” President Obama recently said in an interview with WIRED. “Whether it’s encouraging people to vote or dislodging Big Data so that people can use it more easily, or getting their forms processed online more simply — there’s a huge amount of work to drag the federal government and state governments and local governments into the 21st century.”

…expanding the discussion around Artificial Intelligence and regulatory processes to include how the technology should be leveraged to ensure fairness and responsiveness in the very basic processes of rulemaking – in particular public notices and comments. These technologies could also enable us to consider not just public comments formally submitted to an agency, but the entire universe of statements made through social media posts, blogs, chat boards — and conceivably every other electronic channel of public communication.

Obviously, an anonymous comment on the Internet should not carry the same credibility as a formally submitted, personally signed statement, just as sworn testimony in court holds far greater weight than a grapevine rumor. But so much public discussion today occurs on Facebook pages, in Tweets, on news website comment sections, etc. Anonymous speech enjoys explicit protection under the Constitution, based on a justified expectation that certain sincere statements of sentiment might result in unfair retribution from the government.

Should we simply ignore the valuable insights about actual public sentiment on specific issues made possible through the power of Artificial Intelligence, which can ascertain meaning from an otherwise unfathomable ocean of relevant public conversations? With certain qualifications, I believe Artificial Intelligence, or AI, should absolutely be employed in the critical effort to gain insights from public comments – signed or anonymous.

“In the criminal justice system, some of the biggest concerns with Big Data are the lack of data and the lack of quality data,” the NSTC report authors state. “AI needs good data. If the data is incomplete or biased, AI can exacerbate problems of bias.” As a former federal criminal prosecutor and defense attorney, I am well familiar with the absolute necessity to weigh the relative value of various forms of evidence – or in this case, data…(More)“

Crowdsourcing Gun Violence Research

Curated on Posted on by Stefaan Verhulst

Penn Engineering: “Gun violence is often described as an epidemic, but as visible and shocking as shooting incidents are, epidemiologists who study that particular source of mortality have a hard time tracking them. The Centers for Disease Control is prohibited by federal law from conducting gun violence research, so there is little in the way of centralized infrastructure to monitor where, how,when, why and to whom shootings occur.

Chris Callison-Burch, Aravind K.Joshi Term Assistant Professor in Computer and InformationScience, and graduate studentEllie Pavlick are working to solve this problem.

They have developed the GunViolence Database, which combines machine learning and crowdsourcing techniques to produce a national registry of shooting incidents. Callison-Burch and Pavlick’s algorithm scans thousands of articles from local newspaper and television stations,determines which are about gun violence, then asks everyday people to pullout vital statistics from those articles, compiling that information into a unified, open database.

For natural language processing experts like Callison-Burch and Pavlick, the most exciting prospect of this effort is that it is training computer systems to do this kind of analysis automatically. They recently presented their work on that front at Bloomberg’s Data for Good Exchange conference.

The Gun Violence Database project started in 2014, when it became the centerpiece of Callison-Burch’s “Crowdsourcing and Human Computation”class. There, Pavlick developed a series of homework assignments that challenged undergraduates to develop a classifier that could tell whether a given news article was about a shooting incident.

“It allowed us to teach the things we want students to learn about datascience and natural language processing, while giving them the motivation to do a project that could contribute to the greater good,” says Callison-Burch.

The articles students used to train their classifiers were sourced from “TheGun Report,” a daily blog from New York Times reporters that attempted to catalog shootings from around the country in the wake of the Sandy Hook massacre. Realizing that their algorithmic approach could be scaled up to automate what the Times’ reporters were attempting, the researchers began exploring how such a database could work. They consulted with DouglasWiebe, a Associate Professor of Epidemiology in Biostatistics andEpidemiology in the Perelman School of Medicine, to learn more about what kind of information public health researchers needed to better study gun violence on a societal scale.

From there, the researchers enlisted people to annotate the articles their classifier found, connecting with them through Mechanical Turk, Amazon’scrowdsourcing platform, and their own website, http://gun-violence.org/…(More)”

The Promise of Artificial Intelligence: 70 Real-World Examples

Curated on Posted on by Stefaan Verhulst

Report by the Information Technology & Innovation Foundation: “Artificial intelligence (AI) is on a winning streak. In 2005, five teams successfully completed the DARPA Grand Challenge, a competition held by the Defense Advanced Research Projects Agency to spur development of autonomous vehicles. In 2011, IBM’s Watson system beat out two longtime human champions to win Jeopardy! In 2016, Google DeepMind’s AlphaGo system defeated the 18-time world-champion Go player. And thanks to Apple’s Siri, Microsoft’s Cortana, Google’s Google Assistant, and Amazon’s Alexa, consumers now have easy access to a variety of AI-powered virtual assistants to help manage their daily lives. The potential uses of AI to identify patterns, learn from experience, and find novel solutions to new challenges continue to grow as the technology advances.

Moreover, AI is already having a major positive impact in many different sectors of the global economy and society.  For example, humanitarian organizations are using intelligent chatbots to provide psychological support to Syrian refugees, and doctors are using AI to develop personalized treatments for cancer patients. Unfortunately, the benefits of AI, as well as its likely impact in the years ahead, are vastly underappreciated by policymakers and the public. Moreover, a contrary narrative—that AI raises grave concerns and warrants a precautionary regulatory approach to limit the damages it could cause—has gained prominence, even though it is both wrong and harmful to societal progress.

To showcase the overwhelmingly positive impact of AI, this report describes the major uses of AI and highlights 70 real-world examples of how AI is already generating social and economic benefits. Policymakers should consider these benefits as they evaluate the steps they can take to support the development and adoption of AI….(More)”

Social Machines: The Coming Collision of Artificial Intelligence, Social Networking, and Humanity

Curated on Posted on by Stefaan Verhulst

Book by James Hendler and Alice Mulvehill: “Will your next doctor be a human being—or a machine? Will you have a choice? If you do, what should you know before making it?

This book introduces the reader to the pitfalls and promises of artificial intelligence in its modern incarnation and the growing trend of systems to “reach off the Web” into the real world. The convergence of AI, social networking, and modern computing is creating an historic inflection point in the partnership between human beings and machines with potentially profound impacts on the future not only of computing but of our world.

AI experts and researchers James Hendler and Alice Mulvehill explore the social implications of AI systems in the context of a close examination of the technologies that make them possible. The authors critically evaluate the utopian claims and dystopian counterclaims of prognosticators. Social Machines: The Coming Collision of Artificial Intelligence, Social Networking, and Humanity is your richly illustrated field guide to the future of your machine-mediated relationships with other human beings and with increasingly intelligent machines.

What you’ll learn

• What the concept of a social machine is and how the activities of non-programmers are contributing to machine intelligence• How modern artificial intelligence technologies, such as Watson, are evolving and how they process knowledge from both carefully produced information (such as Wikipedia or journal articles) and from big data collections

• The fundamentals of neuromorphic computing

• The fundamentals of knowledge graph search and linked data as well as the basic technology concepts that underlie networking applications such as Facebook and Twitter

• How the change in attitudes towards cooperative work on the Web, especially in the younger demographic, is critical to the future of Web applications…(More)”

Combining Satellite Imagery and Machine Learning to Predict Poverty

Curated on Posted on by Stefaan Verhulst

From the sustainability and artificial intelligence lab: “The elimination of poverty worldwide is the first of 17 UN Sustainable Development Goals for the year 2030. To track progress towards this goal, we require more frequent and more reliable data on the distribution of poverty than traditional data collection methods can provide.

In this project, we propose an approach that combines machine learning with high-resolution satellite imagery to provide new data on socioeconomic indicators of poverty and wealth. Check out the short video below for a quick overview and then read the paper for a more detailed explanation of how it all works….(More)”

Artificial intelligence is hard to see

Curated on Posted on by Stefaan Verhulst

Kate Crawford and Meredith Whittaker on “Why we urgently need to measure AI’s societal impacts“: “How will artificial intelligence systems change the way we live? This is a tough question: on one hand, AI tools are producing compelling advances in complex tasks, with dramatic improvements in energy consumption, audio processing, and leukemia detection. There is extraordinary potential to do much more in the future. On the other hand, AI systems are already making problematic judgements that are producing significant social, cultural, and economic impacts in people’s everyday lives.

AI and decision-support systems are embedded in a wide array of social institutions, from influencing who is released from jail to shaping the news we see. For example, Facebook’s automated content editing system recently censored the Pulitzer-prize winning image of a nine-year old girl fleeing napalm bombs during the Vietnam War. The girl is naked; to an image processing algorithm, this might appear as a simple violation of the policy against child nudity. But to human eyes, Nick Ut’s photograph, “The Terror of War”, means much more: it is an iconic portrait of the indiscriminate horror of conflict, and it has an assured place in the history of photography and international politics. The removal of the image caused an international outcry before Facebook backed down and restored the image. “What they do by removing such images, no matter what good intentions, is to redact our shared history,” said the Prime Minister of Norway, Erna Solberg.

It’s easy to forget that these high-profile instances are actually the easy cases. As Tarleton Gillespie has observed, hundreds of content reviews are occurring with Facebook images thousand of times per day, and rarely is there a Pulitzer prize to help determine lasting significance. Some of these reviews include human teams, and some do not. In this case, there is alsoconsiderable ambiguity about where the automated process ended and the human review began: which is part of the problem. And Facebook is just one player in complex ecology of algorithmically-supplemented determinations with little external monitoring to see how decisions are made or what the effects might be.

The ‘Terror of War’ case, then, is the tip of the iceberg: a rare visible instance that points to a much larger mass of unseen automated and semi-automated decisions. The concern is that most of these ‘weak AI’ systems are making decisions that don’t garner such attention. They are embedded at the back-end of systems, working at the seams of multiple data sets, with no consumer-facing interface. Their operations are mainly unknown, unseen, and with impacts that take enormous effort to detect.

Sometimes AI techniques get it right, and sometimes they get it wrong. Only rarely will those errors be seen by the public: like the Vietnam war photograph, or when a AI ‘beauty contest’ held this month was called out for being racist for selecting white women as the winners. We can dismiss this latter case as a problem of training data — they simply need a more diverse selection of faces to train their algorithm with, and now that 600,000 people have sent in their selfies, they certainly have better means to do so. But while a beauty contest might seem like a bad joke, or just a really good trick to get people to give up their photos to build a large training data set, it points to a much bigger set of problems. AI and decision-support systems are reaching into everyday life: determining who will be on a predictive policing‘heat list’, who will be hired or promoted, which students will be recruited to universities, or seeking to predict at birth who will become a criminal by the age of 18. So the stakes are high…(More)”

Law in the Future

Curated on Posted on by Stefaan Verhulst

Paper by Benjamin Alarie, Anthony Niblett and Albert Yoon: “The set of tasks and activities in which humans are strictly superior to computers is becoming vanishingly small. Machines today are not only performing mechanical or manual tasks once performed by humans, they are also performing thinking tasks, where it was long believed that human judgment was indispensable. From self-driving cars to self-flying planes; and from robots performing surgery on a pig to artificially intelligent personal assistants, so much of what was once unimaginable is now reality. But this is just the beginning of the big data and artificial intelligence revolution. Technology continues to improve at an exponential rate. How will the big data and artificial intelligence revolutions affect law? We hypothesize that the growth of big data, artificial intelligence, and machine learning will have important effects that will fundamentally change the way law is made, learned, followed, and practiced. It will have an impact on all facets of the law, from the production of micro-directives to the way citizens learn of their legal obligations. These changes will present significant challenges to human lawmakers, judges, and lawyers. While we do not attempt to address all these challenges, we offer a short and positive preview of the future of law: a world of self-driving law, of legal singularity, and of the democratization of the law…(More)”

A cautionary tale about humans creating biased AI models

Curated on Posted on by Stefaan Verhulst

 at TechCrunch: “Most artificial intelligence models are built and trained by humans, and therefore have the potential to learn, perpetuate and massively scale the human trainers’ biases. This is the word of warning put forth in two illuminating articles published earlier this year by Jack Clark at Bloomberg and Kate Crawford at The New York Times.

Tl;dr: The AI field lacks diversity — even more spectacularly than most of our software industry. When an AI practitioner builds a data set on which to train his or her algorithm, it is likely that the data set will only represent one worldview: the practitioner’s. The resulting AImodel demonstrates a non-diverse “intelligence” at best, and a biased or even offensive one at worst….

So what happens when you don’t consider carefully who is annotating the data? What happens when you don’t account for the differing preferences, tendencies and biases among varying humans? We ran a fun experiment to find out….Actually, we didn’t set out to run an experiment. We just wanted to create something fun that we thought our awesome tasking community would enjoy. The idea? Give people the chance to rate puppies’ cuteness in their spare time…There was a clear gender gap — a very consistent pattern of women rating the puppies as cuter than the men did. The gap between women’s and men’s ratings was more narrow for the “less-cute” (ouch!) dogs, and wider for the cuter ones. Fascinating.

I won’t even try to unpack the societal implications of these findings, but the lesson here is this: If you’re training an artificial intelligence model — especially one that you want to be able to perform subjective tasks — there are three areas in which you must evaluate and consider demographics and diversity:

  • yourself
  • your data
  • your annotators

This was a simple example: binary gender differences explaining one subjective numeric measure of an image. Yet it was unexpected and significant. As our industry deploys incredibly complex models that are pushing to the limit chip sets, algorithms and scientists, we risk reinforcing subtle biases, powerfully and at a previously unimaginable scale. Even more pernicious, many AIs reinforce their own learning, so we need to carefully consider “supervised” (aka human) re-training over time.

Artificial intelligence promises to change all of our lives — and it already subtly guides the way we shop, date, navigate, invest and more. But to make sure that it does so for the better, all of us practitioners need to go out of our way to be inclusive. We need to remain keenly aware of what makes us all, well… human. Especially the subtle, hidden stuff….(More)”

Encouraging and Sustaining Innovation in Government: Technology and Innovation in the Next Administration

Curated on Posted on by Stefaan Verhulst

New report by Beth Simone Noveck and Stefaan Verhulst: “…With rates of trust in government at an all-time low, technology and innovation will be essential to achieve the next administration’s goals and to deliver services more effectively and efficiently. The next administration must prioritize using technology to improve governing and must develop plans to do so in the transition… This paper provides analysis and a set of concrete recommendations, both for the period of transition before the inauguration, and for the start of the next presidency, to encourage and sustain innovation in government. Leveraging the insights from the experts who participated in a day-long discussion, we endeavor to explain how government can improve its use of using digital technologies to create more effective policies, solve problems faster and deliver services more effectively at the federal, state and local levels….

The broad recommendations are:

  • Scale Data Driven Governance: Platforms such as data.gov represent initial steps in the direction of enabling data-driven governance. Much more can be done, however, to open-up data and for the agencies to become better consumers of data, to improve decision-making and scale up evidence-based governance. This includes better use of predictive analytics, more public engagement; and greater use of cutting-edge methods like machine learning.
  • Scale Collaborative Innovation: Collaborative innovation takes place when government and the public work together, thus widening the pool of expertise and knowledge brought to bear on public problems. The next administration can reach out more effectively, not just to the public at large, but to conduct targeted outreach to public officials and citizens who possess the most relevant skills or expertise for the problems at hand.
  • Promote a Culture of Innovation: Institutionalizing a culture of technology-enabled innovation will require embedding and institutionalizing innovation and technology skills more widely across the federal enterprise. For example, contracting, grants and personnel officials need to have a deeper understanding of how technology can help them do their jobs more efficiently, and more people need to be trained in human-centered design, gamification, data science, data visualization, crowdsourcing and other new ways of working.
  • Utilize Evidence-Based Innovation: In order to better direct government investments, leaders need a much better sense of what works and what doesn’t. The government spends billions on research in the private and university sectors, but very little experimenting with, testing, and evaluating its own programs. The next administration should continue developing an evidence-based approach to governance, including a greater use of methods like A/B testing (a method of comparing two versions of a webpage or app against each other to determine which one performs the best); establishing a clearinghouse for success and failure stories and best practices; and encouraging overseers to be more open to innovation.
  • Make Innovation a Priority in the Transition: The transition period represents a unique opportunity to seed the foundations for long-lasting change. By explicitly incorporating innovation into the structure, goals and activities of the transition teams, the next administration can get a fast start in implementing policy goals and improving government operations through innovation approaches….(More)”

How the Federal Government is thinking about Artificial Intelligence

Curated on Posted on by Stefaan Verhulst

Mohana Ravindranath at NextGov: “Since May, the White House has been exploring the use of artificial intelligence and machine learning for the public: that is, how the federal government should be investing in the technology to improve its own operations. The technologies, often modeled after the way humans take in, store and use new information, could help researchers find patterns in genetic data or help judges decide sentences for criminals based on their likelihood to end up there again, among other applications. …

Here’s a look at how some federal groups are thinking about the technology:

  • Police data: At a recent White House workshop, Office of Science and Technology Policy Senior Adviser Lynn Overmann said artificial intelligence could help police departments comb through hundreds of thousands of hours of body-worn camera footage, potentially identifying the police officers who are good at de-escalating situations. It also could help cities determine which individuals are likely to end up in jail or prison and officials could rethink programs. For example, if there’s a large overlap between substance abuse and jail time, public health organizations might decide to focus their efforts on helping people reduce their substance abuse to keep them out of jail.
  • Explainable artificial intelligence: The Pentagon’s research and development agency is looking for technology that can explain to analysts how it makes decisions. If people can’t understand how a system works, they’re not likely to use it, according to a broad agency announcement from the Defense Advanced Research Projects Agency. Intelligence analysts who might rely on a computer for recommendations on investigative leads must “understand why the algorithm has recommended certain activity,” as do employees overseeing autonomous drone missions.
  • Weather detection: The Coast Guard recently posted its intent to sole-source a contract for technology that could autonomously gather information about traffic, crosswind, and aircraft emergencies. That technology contains built-in artificial intelligence technology so it can “provide only operational relevant information.”
  • Cybersecurity: The Air Force wants to make cyber defense operations as autonomous as possible, and is looking at artificial intelligence that could potentially identify or block attempts to compromise a system, among others.

While there are endless applications in government, computers won’t completely replace federal employees anytime soon….(More)”