Digital Equity 2.0: How to Close the Data Divide

Curated on Posted on by Stefaan Verhulst

Report by Gillian Diebold: “For the last decade, closing the digital divide, or the gap between those subscribing to broadband and those not subscribing, has been a top priority for policymakers. But high-speed Internet and computing device access are no longer the only barriers to fully participating and benefiting from the digital economy. Data is also increasingly essential, including in health care, financial services, and education. Like the digital divide, a gap has emerged between the data haves and the data have-nots, and this gap has introduced a new set of inequities: the data divide.

Policymakers have put a great deal of effort into closing the digital divide, and there is now near-universal acceptance of the notion that obtaining widespread Internet access generates social and economic benefits. But closing the data divide has received little attention. Moreover, efforts to improve data collection are typically overshadowed by privacy advocates’ warnings against collecting any data. In fact, unlike the digital divide, many ignore the data divide or argue that the way to close it is to collect vastly less data.1 But without substantial efforts to increase data representation and access, certain individuals and communities will be left behind in an increasingly data-driven world.

This report describes the multipronged efforts needed to address digital inequity. For the digital divide, policymakers have expanded digital connectivity, increased digital literacy, and improved access to digital devices. For the data divide, policymakers should similarly take a holistic approach, including by balancing privacy and data innovation, increasing data collection efforts across a wide array of fronts, enhancing access to data, improving data quality, and improving data analytics efforts. Applying lessons from the digital divide to this new challenge will help policymakers design effective and efficient policy and create a more equitable and effective data economy for all Americans…(More)”.

Let’s Randomize America! 

Curated on Posted on by Stefaan Verhulst

Article by Dalton Conley: “…As our society has become less random, it has become more unequal. Many people know that inequality has been rising steadily over time, but a less-remarked-on development is that there’s been a parallel geographic shift, with high- and low-income people moving into separate, ever more distinct communities…As a sociologist, I study inequality and what can be done about it. It is, to say the least, a difficult problem to solve…I’ve come to believe that lotteries could help to crack this nut and make our society fairer and more equal. We can’t randomly assign where people live, of course. And we can’t integrate neighborhoods by fiat, either. We learned that lesson in the nineteen-seventies, when counties tried busing schoolchildren across town. Those programs aimed to create more racially and economically integrated schools; they resulted in the withdrawal of affluent students from urban public-school systems, and set off a political backlash that can still be felt today…

As a political tool, lotteries have come and gone throughout history. Sortition—the selection of political officials by lot—was first practiced in Athens in the sixth century B.C.E., and later reappeared in Renaissance city-states such as Florence, Venice, and Lombardy, and in Switzerland and elsewhere. In recent years, citizens’ councils—randomly chosen groups of individuals who meet to hammer out a particular issue, such as climate policy—have been tried in Canada, France, Iceland, Ireland, and the U.K. Some political theorists, such as Hélène Landemore, Jane Mansbridge, and the Belgian writer David Van Reybrouck, have argued that randomly selected decision-makers who don’t have to campaign are less likely to be corrupt or self-interested than those who must run for office; people chosen at random are also unlikely to be typically privileged, power-hungry politicians. The wisdom of the crowd improves when the crowd is more diverse…(More)”.

Machines of mind: The case for an AI-powered productivity boom

Curated on Posted on by Stefaan Verhulst

Report by Martin Neil Baily, Erik Brynjolfsson, Anton Korinek: “ Large language models such as ChatGPT are emerging as powerful tools that not only make workers more productive but also increase the rate of innovation, laying the foundation for a significant acceleration in economic growth. As a general purpose technology, AI will impact a wide array of industries, prompting investments in new skills, transforming business processes, and altering the nature of work. However, official statistics will only partially capture the boost in productivity because the output of knowledge workers is difficult to measure. The rapid advances can have great benefits but may also lead to significant risks, so it is crucial to ensure that we steer progress in a direction that benefits all of society…(More)”.

Data portability and interoperability: A primer on two policy tools for regulation of digitized industries

Curated on Posted on by Stefaan Verhulst

Article by Sukhi Gulati-Gilbert and Robert Seamans: “…In this article we describe two other tools, data portability and interoperability, that may be particularly useful in technology-enabled sectors. Data portability allows users to move data from one company to another, helping to reduce switching costs and providing rival firms with access to valuable customer data. Interoperability allows two or more technical systems to exchange data interactively. Due to its interactive nature, interoperability can help prevent lock-in to a specific platform by allowing users to connect across platforms. Data portability and interoperability share some similarities; in addition to potential pro-competitive benefits, the tools promote values of openness, transparency, and consumer choice.

After providing an overview of these topics, we describe the tradeoffs involved with implementing data portability and interoperability. While these policy tools offer lots of promise, in practice there can be many challenges involved when determining how to fund and design an implementation that is secure and intuitive and accomplishes the intended result.  These challenges require that policymakers think carefully about the initial implementation of data portability and interoperability. Finally, to better show how data portability and interoperability can increase competition in an industry, we discuss how they could be applied in the banking and social media sectors. These are just two examples of how data portability and interoperability policy could be applied to many different industries facing increased digitization. Our definitions and examples should be helpful to those interested in understanding the tradeoffs involved in using these tools to promote competition and innovation in the U.S. economy…(More)” See also: Data to Go: The Value of Data Portability as a Means to Data Liquidity.

Evidence Gap Maps as Critical Information Communication Devices for Evidence-based Public Policy

Curated on Posted on by Stefaan Verhulst

Paper by Esteban Villa-Turek et al: “The public policy cycle requires increasingly the use of evidence by policy makers. Evidence Gap Maps (EGMs) are a relatively new methodology that helps identify, process, and visualize the vast amounts of studies representing a rich source of evidence for better policy making. This document performs a methodological review of EGMs and presents the development of a working integrated system that automates several critical steps of EGM creation by means of applied computational and statistical methods. Above all, the proposed system encompasses all major steps of EGM creation in one place, namely inclusion criteria determination, processing of information, analysis, and user-friendly communication of synthesized relevant evidence. This tool represents a critical milestone in the efforts of implementing cutting-edge computational methods in usable systems. The contribution of the document is two-fold. First, it presents the critical importance of EGMs in the public policy cycle; second, it justifies and explains the development of a usable tool that encompasses the methodological phases of creation of EGMs, while automating most time-consuming stages of the process. The overarching goal is the better and faster information communication to relevant actors like policy makers, thus promoting well-being through better and more efficient interventions based on more evidence-driven policy making…(More)”.

The People and the Experts

Curated on Posted on by Stefaan Verhulst

Paper by William D. Nordhaus & Douglas Rivers: “Are speculators driving up oil prices? Should we raise energy prices to slow global warming? The present study takes a small number of such questions and compares the views of economic experts with those of the public. This comparison uses a panel of more than 2000 respondents from YouGov with the views of the panel of experts from the Initiative on Global Markets at the Chicago Booth School. We found that most of the US population is at best modestly informed about major economic questions and policies. The low level of knowledge is generally associated with the intrusion of ideological, political, and religious views that challenge or deny the current economic consensus. The intruding factors are highly heterogeneous across questions and sub-populations and are much more diverse than the narrowness of public political discourse would suggest. Many of these findings have been established for scientific subjects, but they appear to be equally important for economic views…(More)”.

AI Is Tearing Wikipedia Apart

Curated on Posted on by Stefaan Verhulst

Article by Claire Woodcock: “As generative artificial intelligence continues to permeate all aspects of culture, the people who steward Wikipedia are divided on how best to proceed. 

During a recent community call, it became apparent that there is a community split over whether or not to use large language models to generate content. While some people expressed that tools like Open AI’s ChatGPT could help with generating and summarizing articles, others remained wary. 

The concern is that machine-generated content has to be balanced with a lot of human review and would overwhelm lesser-known wikis with bad content. While AI generators are useful for writing believable, human-like text, they are also prone to including erroneous information, and even citing sources and academic papers which don’t exist. This often results in text summaries which seem accurate, but on closer inspection are revealed to be completely fabricated. 

“The risk for Wikipedia is people could be lowering the quality by throwing in stuff that they haven’t checked,” Bruckman added. “I don’t think there’s anything wrong with using it as a first draft, but every point has to be verified.” 

The Wikimedia Foundation, the nonprofit organization behind the website, is looking into building tools to make it easier for volunteers to identify bot-generated content. Meanwhile, Wikipedia is working to draft a policy that lays out the limits to how volunteers can use large language models to create content.

The current draft policy notes that anyone unfamiliar with the risks of large language models should avoid using them to create Wikipedia content, because it can open the Wikimedia Foundation up to libel suits and copyright violations—both of which the nonprofit gets protections from but the Wikipedia volunteers do not. These large language models also contain implicit biases, which often result in content skewed against marginalized and underrepresented groups of people. 

The community is also divided on whether large language models should be allowed to train on Wikipedia content. While open access is a cornerstone of Wikipedia’s design principles, some worry the unrestricted scraping of internet data allows AI companies like OpenAI to exploit the open web to create closed commercial datasets for their models. This is especially a problem if the Wikipedia content itself is AI-generated, creating a feedback loop of potentially biased information, if left unchecked…(More)”.

Will A.I. Become the New McKinsey?

Curated on Posted on by Stefaan Verhulst

Essay by Ted Chiang: “When we talk about artificial intelligence, we rely on metaphor, as we always do when dealing with something new and unfamiliar. Metaphors are, by their nature, imperfect, but we still need to choose them carefully, because bad ones can lead us astray. For example, it’s become very common to compare powerful A.I.s to genies in fairy tales. The metaphor is meant to highlight the difficulty of making powerful entities obey your commands; the computer scientist Stuart Russell has cited the parable of King Midas, who demanded that everything he touched turn into gold, to illustrate the dangers of an A.I. doing what you tell it to do instead of what you want it to do. There are multiple problems with this metaphor, but one of them is that it derives the wrong lessons from the tale to which it refers. The point of the Midas parable is that greed will destroy you, and that the pursuit of wealth will cost you everything that is truly important. If your reading of the parable is that, when you are granted a wish by the gods, you should phrase your wish very, very carefully, then you have missed the point.

So, I would like to propose another metaphor for the risks of artificial intelligence. I suggest that we think about A.I. as a management-consulting firm, along the lines of McKinsey & Company. Firms like McKinsey are hired for a wide variety of reasons, and A.I. systems are used for many reasons, too. But the similarities between McKinsey—a consulting firm that works with ninety per cent of the Fortune 100—and A.I. are also clear. Social-media companies use machine learning to keep users glued to their feeds. In a similar way, Purdue Pharma used McKinsey to figure out how to “turbocharge” sales of OxyContin during the opioid epidemic. Just as A.I. promises to offer managers a cheap replacement for human workers, so McKinsey and similar firms helped normalize the practice of mass layoffs as a way of increasing stock prices and executive compensation, contributing to the destruction of the middle class in America…(More)”.

Spamming democracy

Curated on Posted on by Stefaan Verhulst

Article by Natalie Alms: “The White House’s Office of Information and Regulatory Affairs is considering AI’s effect in the regulatory process, including the potential for generative chatbots to fuel mass campaigns or inject spam comments into the federal agency rulemaking process.

A recent executive order directed the office to consider using guidance or tools to address mass comments, computer-generated comments and falsely attributed comments, something an administration official told FCW that OIRA is “moving forward” on.

Mark Febrezio, a senior policy analyst at George Washington University’s Regulatory Studies Center, has experimented with Open AI’s generative AI system ChatGPT to create what he called a “convincing” public comment submission to a Labor Department proposal. 

“Generative AI also takes the possibility of mass and malattributed comments to the next level,” wrote Fabrizio and co-author Bridget Dooling, research professor at the center, in a paper published in April by the Brookings Institution.

The executive order comes years after astroturfing during the rollback of net neutrality policies by the Federal Communications Commission in 2017 garnered public attention. That rulemaking docket received a record-breaking 22 million-plus comments, but over 8.5 million came from a campaign against net neutrality led by broadband companies, according to an investigation by the New York Attorney General released in 2021. 

The investigation found that lead generators paid by these companies submitted many comments with real names and addresses attached without the knowledge or consent of those individuals.  In the same docket were over 7 million comments supporting net neutrality submitted by a computer science student, who used software to submit comments attached to computer-generated names and addresses.

While the numbers are staggering, experts told FCW that agencies aren’t just counting comments when reading through submissions from the public…(More)”

Unlocking the Power of Data Refineries for Social Impact

Curated on Posted on by Stefaan Verhulst

Essay by Jason Saul & Kriss Deiglmeier: “In 2021, US companies generated $2.77 trillion in profits—the largest ever recorded in history. This is a significant increase since 2000 when corporate profits totaled $786 billion. Social progress, on the other hand, shows a very different picture. From 2000 to 2021, progress on the United Nations Sustainable Development Goals has been anemic, registering less than 10 percent growth over 20 years.

What explains this massive split between the corporate and the social sectors? One explanation could be the role of data. In other words, companies are benefiting from a culture of using data to make decisions. Some refer to this as the “data divide”—the increasing gap between the use of data to maximize profit and the use of data to solve social problems…

Our theory is that there is something more systemic going on. Even if nonprofit practitioners and policy makers had the budget, capacity, and cultural appetite to use data; does the data they need even exist in the form they need it? We submit that the answer to this question is a resounding no. Usable data doesn’t yet exist for the sector because the sector lacks a fully functioning data ecosystem to create, analyze, and use data at the same level of effectiveness as the commercial sector…(More)”.