SAM, the first A.I. politician on Messenger

Curated on Posted on by Stefaan Verhulst

 at Digital Trends: “It’s said that all politicians are the same, but it seems safe to assume that you’ve never seen a politician quite like this. Meet SAM, heralded as the politician of the future. Unfortunately, you can’t exactly shake this politician’s hand, or have her kiss your baby. Rather, SAM is the world’s first Virtual Politician (and a female presence at that), “driven by the desire to close the gap between what voters want and what politicians promise, and what they actually achieve.”

The artificially intelligent chat bot is currently live on Facebook Messenger, though she probably is most helpful to those in New Zealand. After all, the bot’s website notes, “SAM’s goal is to act as a representative for all New Zealanders, and evolves based on voter input.” Capable of being reached by anyone at just about anytime from anywhere, this may just be the single most accessible politician we’ve ever seen. But more importantly, SAM purports to be a true representative, claiming to analyze “everyone’s views [and] opinions, and impact of potential decisions.” This, the bot notes, could make for better policy for everyone….(More)”.

Nearly All of Wikipedia Is Written By Just 1 Percent of Its Editors

Curated on Posted on by Stefaan Verhulst

Daniel Oberhaus at Motherboard: “…Sixteen years later, the free encyclopedia and fifth most popular website in the world is well on its way to this goal. Today, Wikipedia is home to 43 million articles in 285 languages and all of these articles are written and edited by an autonomous group of international volunteers.

Although the non-profit Wikimedia Foundation diligently keeps track of how editors and users interact with the site, until recently it was unclear how content production on Wikipedia was distributed among editors. According to the results of a recent study that looked at the 250 million edits made on Wikipedia during its first ten years, only about 1 percent of Wikipedia’s editors have generated 77 percent of the site’s content.

“Wikipedia is both an organization and a social movement,” Sorin Matei, the director of the Purdue University Data Storytelling Network and lead author of the study, told me on the phone. “The assumption is that it’s a creation of the crowd, but this couldn’t be further from the truth. Wikipedia wouldn’t have been possible without a dedicated leadership.”

At the time of writing, there are roughly 132,000 registered editors who have been active on Wikipedia in the last month (there are also an unknown number of unregistered Wikipedians who contribute to the site). So statistically speaking, only about 1,300 people are creating over three-quarters of the 600 new articles posted to Wikipedia every day.

Of course, these “1 percenters” have changed over the last decade and a half. According to Matei, roughly 40 percent of the top 1 percent of editors bow out about every five weeks. In the early days, when there were only a few hundred thousand people collaborating on Wikipedia, Matei said the content production was significantly more equitable. But as the encyclopedia grew, and the number of collaborators grew with it, a cadre of die-hard editors emerged that have accounted for the bulk of Wikipedia’s growth ever since.

Matei and his colleague Brian Britt, an assistant professor of journalism at South Dakota State University, used a machine learning algorithm to crawl the quarter of a billion publicly available edit logs from Wikipedia’s first decade of existence. The results of this research, published September as a book, suggests that for all of Wikipedia’s pretension to being a site produced by a network of freely collaborating peers, “some peers are more equal than others,” according to Matei.

Matei and Britt argue that rather than being a decentralized, spontaneously evolving organization, Wikipedia is better described as an “adhocracy“—a stable hierarchical power structure which nevertheless allows for a high degree of individual mobility within that hierarchy….(More)”.

More Machine Learning About Congress’ Priorities

Curated on Posted on by Stefaan Verhulst

ProPublica: “We keep training machine learning models on Congress. Find out what this one learned about lawmakers’ top issues…

Speaker of the House Paul Ryan is a tax wonk ― and most observers of Congress know that. But knowing what interests the other 434 members of Congress is harder.

To make it easier to know what issues each lawmaker really focuses on, we’re launching a new feature in our Represent database called Policy Priorities. We had two goals in creating it: To help researchers and journalists understand what drives particular members of Congress and to enable regular citizens to compare their representatives’ priorities to their own and their communities.

We created Policy Priorities using some sophisticated computer algorithms (more on this in a second) to calculate interest based on what each congressperson talks ― and brags ― about in their press releases.

Voting and drafting legislation aren’t the only things members of Congress do with their time, but they’re often the main way we analyze congressional data, in part because they’re easily measured. But the job of a member of Congress goes well past voting. They go to committee meetings, discuss policy on the floor and in caucuses, raise funds and ― important for our purposes ― communicate with their constituents and journalists back home. They use press releases to talk about what they’ve accomplished and to demonstrate their commitment to their political ideals.

We’ve been gathering these press releases for a few years, and have a body of some 86,000 that we used for a kind of analysis called machine learning….(More)”.

Leveraging the disruptive power of artificial intelligence for fairer opportunities

Curated on Posted on by Stefaan Verhulst

Makada Henry-Nickie at Brookings: “According to President Obama’s Council of Economic Advisers (CEA), approximately 3.1 million jobs will be rendered obsolete or permanently altered as a consequence of artificial intelligence technologies. Artificial intelligence (AI) will, for the foreseeable future, have a significant disruptive impact on jobs. That said, this disruption can create new opportunities if policymakers choose to harness them—including some with the potential to help address long-standing social inequities. Investing in quality training programs that deliver premium skills, such as computational analysis and cognitive thinking, provides a real opportunity to leverage AI’s disruptive power.

AI’s disruption presents a clear challenge: competition to traditional skilled workers arising from the cross-relevance of data scientists and code engineers, who can adapt quickly to new contexts. Data analytics has become an indispensable feature of successful companies across all industries. ….

Investing in high-quality education and training programs is one way that policymakers proactively attempt to address the workforce challenges presented by artificial intelligence. It is essential that we make affirmative, inclusive choices to ensure that marginalized communities participate equitably in these opportunities.

Policymakers should prioritize understanding the demographics of those most likely to lose jobs in the short-run. As opposed to obsessively assembling case studies, we need to proactively identify policy entrepreneurs who can conceive of training policies that equip workers with technical skills of “long-game” relevance. As IBM points out, “[d]ata democratization impacts every career path, so academia must strive to make data literacy an option, if not a requirement, for every student in any field of study.”

Machines are an equal opportunity displacer, blind to color and socioeconomic status. Effective policy responses require collaborative data collection and coordination among key stakeholders—policymakers, employers, and educational institutions—to  identify at-risk worker groups and to inform workforce development strategies. Machine substitution is purely an efficiency game in which workers overwhelmingly lose. Nevertheless, we can blunt these effects by identifying critical leverage points….

Policymakers can choose to harness AI’s disruptive power to address workforce challenges and redesign fair access to opportunity simultaneously. We should train our collective energies on identifying practical policies that update our current agrarian-based education model, which unfairly disadvantages children from economically segregated neighborhoods…(More)”

When Data Science Destabilizes Democracy and Facilitates Genocide

Curated on Posted on by Stefaan Verhulst

Rachel Thomas in Fast.AI on “What is the ethical responsibility of data scientists?”…What we’re talking about is a cataclysmic change… What we’re talking about is a major foreign power with sophistication and ability to involve themselves in a presidential election and sow conflict and discontent all over this country… You bear this responsibility. You’ve created these platforms. And now they are being misused, Senator Feinstein said this week in a senate hearing. Who has created a cataclysmic change? Who bears this large responsibility? She was talking to executives at tech companies and referring to the work of data scientists.

Data science can have a devastating impact on our world, as illustrated by inflammatory Russian propaganda being shown on Facebook to 126 million Americans leading up to the 2016 election (and the subject of the senate hearing described above) or by lies spread via Facebook that are fueling ethnic cleansing in Myanmar. Over half a million Rohinyga have been driven from their homes due to systematic murder, rape, and burning. Data science is foundational to Facebook’s newsfeed, in determining what content is prioritized and who sees what….

The examples of bias in data science are myriad and include:

You can do awesome and meaningful things with data science (such as diagnosing cancer, stopping deforestation, increasing farm yields, and helping patients with Parkinson’s disease), and you can (often unintentionally) enable terrible things with data science, as the examples in this post illustrate. Being a data scientist entails both great opportunity, as well as great responsibility, to use our skills to not make the world a worse place. Ultimately, doing data science is about humans, not just the users of our products, but everyone who will be impacted by our work. (More)”.

The Challenge of VR to the Liberal Democratic Order

Curated on Posted on by Stefaan Verhulst

Paper by Edward Castronova: “The rapid expansion of virtual reality (VR) technology in the years 2016-2021 awakens a significant constitutional issue. In a liberal democratic order, rule is by consent of the governed. In the medium-term future, many of the governed will be immersed fully within VR environments, environments which, we are told, will provide entertainment of extraordinary power. These people will be happy. Happy people do not demand change. Yet there surely will be a change as VR takes hold: The quality of life will erode. People fully immersed in VR will come to be isolated, sedentary, and unhealthy. Objectively speaking, this is nothing to be desired. Subjectively, however, it will seem to be wonderful. The people themselves will be happy, and they will resist interference. At the moment this matter concerns only a few thousand nerds, but trends in technology and entertainment point to a future in which many people will be happily living awful, VR-dominated lives. How then will the liberal democratic order promote human well-being while remaining a liberal and democratic order?…(More)”

Understanding Corporate Data Sharing Decisions: Practices, Challenges, and Opportunities for Sharing Corporate Data with Researchers

Curated on Posted on by Stefaan Verhulst

Leslie Harris at the Future of Privacy Forum: “Data has become the currency of the modern economy. A recent study projects the global volume of data to grow from about 0.8 zettabytes (ZB) in 2009 to more than 35 ZB in 2020, most of it generated within the last two years and held by the corporate sector.

As the cost of data collection and storage becomes cheaper and computing power increases, so does the value of data to the corporate bottom line. Powerful data science techniques, including machine learning and deep learning, make it possible to search, extract and analyze enormous sets of data from many sources in order to uncover novel insights and engage in predictive analysis. Breakthrough computational techniques allow complex analysis of encrypted data, making it possible for researchers to protect individual privacy, while extracting valuable insights.

At the same time, these newfound data sources hold significant promise for advancing scholarship and shaping more impactful social policies, supporting evidence-based policymaking and more robust government statistics, and shaping more impactful social interventions. But because most of this data is held by the private sector, it is rarely available for these purposes, posing what many have argued is a serious impediment to scientific progress.

A variety of reasons have been posited for the reluctance of the corporate sector to share data for academic research. Some have suggested that the private sector doesn’t realize the value of their data for broader social and scientific advancement. Others suggest that companies have no “chief mission” or public obligation to share. But most observers describe the challenge as complex and multifaceted. Companies face a variety of commercial, legal, ethical, and reputational risks that serve as disincentives to sharing data for academic research, with privacy – particularly the risk of reidentification – an intractable concern. For companies, striking the right balance between the commercial and societal value of their data, the privacy interests of their customers, and the interests of academics presents a formidable dilemma.

To be sure, there is evidence that some companies are beginning to share for academic research. For example, a number of pharmaceutical companies are now sharing clinical trial data with researchers, and a number of individual companies have taken steps to make data available as well. What is more, companies are also increasingly providing open or shared data for other important “public good” activities, including international development, humanitarian assistance and better public decision-making. Some are contributing to data collaboratives that pool data from different sources to address societal concerns. Yet, it is still not clear whether and to what extent this “new era of data openness” will accelerate data sharing for academic research.

Today, the Future of Privacy Forum released a new study, Understanding Corporate Data Sharing Decisions: Practices, Challenges, and Opportunities for Sharing Corporate Data with Researchers. In this report, we aim to contribute to the literature by seeking the “ground truth” from the corporate sector about the challenges they encounter when they consider making data available for academic research. We hope that the impressions and insights gained from this first look at the issue will help formulate further research questions, inform the dialogue between key stakeholders, and identify constructive next steps and areas for further action and investment….(More)”.

Bot.Me: A revolutionary partnership

Curated on Posted on by Stefaan Verhulst

PWC Consumer Intelligence Series: “The modern world has been shaped by the technological revolutions of the past, like the Industrial Revolution and the Information Revolution. The former redefined the way the world values both human and material resources; the latter redefined value in terms of resources while democratizing information. Today, as technology progresses even further, value is certain to shift again, with a focus on sentiments more intrinsic to the human experience: thinking, creativity, and problem-solving. AI, shorthand for artificial intelligence, defines technologies emerging today that can understand, learn, and then act based on that information. Forms of AI in use today include digital assistants, chatbots, and machine learning.

Today, AI works in three ways:

  • Assisted intelligence, widely available today, improves what people and organizations are already doing. A simple example, prevalent in cars today, is the GPS navigation program that offers directions to drivers and adjusts to road conditions.
  • Augmented intelligence, emerging today, enables people and organizations to do things they couldn’t otherwise do. For example, the combination of programs that organize cars in ride-sharing services enables businesses that could not otherwise exist.
  • Autonomous intelligence, being developed for the future, establishes machines that act on their own. An example of this will be self-driving vehicles, when they come into widespread use.

With a market projected to reach $70 billion by 2020, AI is poised to have a transformative effect on consumer, enterprise, and government markets around the world. While there are certainly obstacles to overcome, consumers believe that AI has the potential to assist in medical breakthroughs, democratize costly services, elevate poor customer service, and even free up an overburdened workforce. Some tech optimists believe AI could create a world where human abilities are amplified as machines help mankind process, analyze, and evaluate the abundance of data that creates today’s world, allowing humans to spend more time engaged in high-level thinking, creativity, and decision-making. Technological revolutions, like the Industrial Revolution and the Information Revolution, didn’t happen overnight. In fact, people in the midst of those revolutions often didn’t even realize they were happening, until history was recorded later.

That is where we find ourselves today, in the very beginning of what some are calling the “augmented age.” Just like humans in the past, it is up to mankind to find the best ways to leverage these machine revolutions to help the world evolve. As Isaac Asimov, the prolific science fiction writer with many works on AI mused, “No sensible decision can be made any longer without taking into account not only the world as it is, but the world as it will be.” As a future with AI approaches, it’s important to understand how people think of it today, how it will amplify the world tomorrow, and what guiding principles will be required to navigate this monumental change….(More)”.

Augmented CI and Human-Driven AI: How the Intersection of Artificial Intelligence and Collective Intelligence Could Enhance Their Impact on Society

Curated on Posted on by Stefaan Verhulst

Blog by Stefaan Verhulst: “As the technology, research and policy communities continue to seek new ways to improve governance and solve public problems, two new types of assets are occupying increasing importance: data and people. Leveraging data and people’s expertise in new ways offers a path forward for smarter decisions, more innovative policymaking, and more accountability in governance. Yet, unlocking the value of these two assets not only requires increased availability and accessibility (through, for instance, open data or open innovation), it also requires innovation in methodology and technology.

The first of these innovations involves Artificial Intelligence (AI). AI offers unprecedented abilities to quickly process vast quantities of data that can provide data-driven insights to address public needs. This is the role it has for example played in New York City, where FireCast, leverages data from across the city government to help the Fire Department identify buildings with the highest fire risks. AI is also considered to improve education, urban transportation,  humanitarian aid and combat corruption, among other sectors and challenges.

The second area is Collective Intelligence (CI). Although it receives less attention than AI, CI offers similar potential breakthroughs in changing how we govern, primarily by creating a means for tapping into the “wisdom of the crowd” and allowing groups to create better solutions than even the smartest experts working in isolation could ever hope to achieve. For example, in several countries patients’ groups are coming together to create new knowledge and health treatments based on their experiences and accumulated expertise. Similarly, scientists are engaging citizens in new ways to tap into their expertise or skills, generating citizen science – ranging from mapping our solar system to manipulating enzyme models in a game-like fashion.

Neither AI nor CI offer panaceas for all our ills; they each pose certain challenges, and even risks.  The effectiveness and accuracy of AI relies substantially on the quality of the underlying data as well as the human-designed algorithms used to analyse that data. Among other challenges, it is becoming increasingly clear how biases against minorities and other vulnerable populations can be built into these algorithms. For instance, some AI-driven platforms for predicting criminal recidivism significantly over-estimate the likelihood that black defendants will commit additional crimes in comparison to white counterparts. (for more examples, see our reading list on algorithmic scrutiny).

In theory, CI avoids some of the risks of bias and exclusion because it is specifically designed to bring more voices into a conversation. But ensuring that that multiplicity of voices adds value, not just noise, can be an operational and ethical challenge. As it stands, identifying the signal in the noise in CI initiatives can be time-consuming and resource intensive, especially for smaller organizations or groups lacking resources or technical skills.

Despite these challenges, however, there exists a significant degree of optimism  surrounding both these new approaches to problem solving. Some of this is hype, but some of it is merited—CI and AI do offer very real potential, and the task facing both policymakers, practitioners and researchers is to find ways of harnessing that potential in a way that maximizes benefits while limiting possible harms.

In what follows, I argue that the solution to the challenge described above may involve a greater interaction between AI and CI. These two areas of innovation have largely evolved and been researched separately until now. However, I believe that there is substantial scope for integration, and mutual reinforcement. It is when harnessed together, as complementary methods and approaches, that AI and CI can bring the full weight of technological progress and modern data analytics to bear on our most complex, pressing problems.

To deconstruct that statement, I propose three premises (and subsequent set of research questions) toward establishing a necessary research agenda on the intersection of AI and CI that can build more inclusive and effective approaches to governance innovation.

Premise I: Toward Augmented Collective Intelligence: AI will enable CI to scale

Premise II: Toward Human-Driven Artificial Intelligence: CI will humanize AI

Premise III: Open Governance will drive a blurring between AI and CI

…(More)”.

Democracy Needs a Reboot for the Age of Artificial Intelligence

Curated on Posted on by Stefaan Verhulst

Katharine Dempsey at The Nation: “…A healthy modern democracy requires ordinary citizens to participate in public discussions about rapidly advancing technologies. We desperately need new policies, regulations, and safety nets for those displaced by machines. With computing power accelerating exponentially, the scale of AI’s significance is still not being fully internalized. The 2017 McKinsey Global Initiative report “A Future that Works” predicts that AI and advanced robotics could automate roughly half of all work globally by 2055, but, McKinsey notes, “this could happen up to 20 years earlier or later depending on the various factors, in addition to other wider economic conditions.”

Granted, the media are producing more articles focused on artificial intelligence, but too often these pieces veer into hysterics. Wired magazine labeled this year’s coverage “The Great Tech Panic of 2017.” We need less fear-mongering and more rational conversation. Dystopian narratives, while entertaining, can also be disorienting. Skynet from the Terminatormovies is not imminent. But that doesn’t mean there aren’t hazards ahead….

Increasingly, to thoughtfully discuss ethics, politics, or business, the general population needs to pay attention to AI. In 1989, Ursula Franklin, the distinguished German-Canadian experimental physicist, delivered a series of lectures titled “The Real World of Technology.” Franklin opened her lectures with an important observation: “The viability of technology, like democracy, depends in the end on the practice of justice and on the enforcements of limits to power.”

For Franklin, technology is not a neutral set of tools; it can’t be divorced from society or values. Franklin further warned that “prescriptive technologies”—ones that isolate tasks, such as factory-style work—find their way into our social infrastructures and create modes of compliance and orthodoxy. These technologies facilitate top-down control….(More)”.