The Center for Public Integrity: “Do you know if a bill introduced in your statehouse — it might govern who can fix your shattered iPhone screen or whether you can still sue a pedophile priest years later — was actually written by your elected lawmakers? Use this new tool to find out.
Spoiler alert The answer may well be no.
Thousands of pieces of “model legislation” are drafted each year by business organizations and special interest groups and distributed to state lawmakers for introduction. These copycat bills influence policymaking across the nation, state by state, often with little scrutiny. This news application was developed by the Center for Public Integrity, part of a year-long collaboration with USA TODAY and the Arizona Republic to bring the practice into the light….(More)”.
Meaningful Inefficiencies: Civic Design in an Age of Digital Expediency
Book by Eric Gordon and Gabriel Mugar: “Public trust in the institutions that mediate civic life-from governing bodies to newsrooms-is low. In facing this challenge, many organizations assume that ensuring greater efficiency will build trust. As a result, these organizations are quick to adopt new technologies to enhance what they do, whether it’s a new app or dashboard. However, efficiency, or charting a path to a goal with the least amount of friction, is not itself always built on a foundation of trust.
Meaningful Inefficiencies is about the practices undertaken by civic designers that challenge the normative applications of “smart technologies” in order to build or repair trust with publics. Based on over sixty interviews with change makers in public serving organizations throughout the United States, as well as detailed case studies, this book provides a practical and deeply philosophical picture of civic life in transition. The designers in this book are not professional designers, but practitioners embedded within organizations who have adopted an approach to public engagement Eric Gordon and Gabriel Mugar call “meaningful inefficiencies,” or the deliberate design of less efficient over more efficient means of achieving some ends. This book illustrates how civic designers are creating meaningful inefficiencies within public serving organizations. It also encourages a rethinking of how innovation within these organizations is understood, applied, and sought after. Different than market innovation, civic innovation is not just about invention and novelty; it is concerned with building communities around novelty, and cultivating deep and persistent trust.
At its core, Meaningful Inefficiencies underlines that good civic innovation will never just involve one single public good, but must instead negotiate a plurality of publics. In doing so, it creates the conditions for those publics to play, resulting in people truly caring for the world. Meaningful Inefficiencies thus presents an emergent and vitally needed approach to creating civic life at a moment when smart and efficient are the dominant forces in social and organizational change….(More)”.
What is My Data Worth?
Ruoxi Jia at Berkeley artificial intelligence research: “People give massive amounts of their personal data to companies every day and these data are used to generate tremendous business values. Some economists and politicians argue that people should be paid for their contributions—but the million-dollar question is: by how much?
This article discusses methods proposed in our recent AISTATS and VLDB papers that attempt to answer this question in the machine learning context. This is joint work with David Dao, Boxin Wang, Frances Ann Hubis, Nezihe Merve Gurel, Nick Hynes, Bo Li, Ce Zhang, Costas J. Spanos, and Dawn Song, as well as a collaborative effort between UC Berkeley, ETH Zurich, and UIUC. More information about the work in our group can be found here.
What are the existing approaches to data valuation?
Various ad-hoc data valuation schemes have been studied in the literature and some of them have been deployed in the existing data marketplaces. From a practitioner’s point of view, they can be grouped into three categories:
- Query-based pricing attaches values to user-initiated queries. One simple example is to set the price based on the number of queries allowed during a time window. Other more sophisticated examples attempt to adjust the price to some specific criteria, such as arbitrage avoidance.
- Data attribute-based pricing constructs a price model that takes into account various parameters, such as data age, credibility, potential benefits, etc. The model is trained to match market prices released in public registries.
- Auction-based pricing designs auctions that dynamically set the price based on bids offered by buyers and sellers.
However, existing data valuation schemes do not take into account the following important desiderata:
- Task-specificness: The value of data depends on the task it helps to fulfill. For instance, if Alice’s medical record indicates that she has disease A, then her data will be more useful to predict disease A as opposed to other diseases.
- Fairness: The quality of data from different sources varies dramatically. In the worst-case scenario, adversarial data sources may even degrade model performance via data poisoning attacks. Hence, the data value should reflect the efficacy of data by assigning high values to data which can notably improve the model’s performance.
- Efficiency: Practical machine learning tasks may involve thousands or billions of data contributors; thus, data valuation techniques should be capable of scaling up.
With the desiderata above, we now discuss a principled notion of data value and computationally efficient algorithms for data valuation….(More)”.
Dollars for Profs: How to Investigate Professors’ Conflicts of Interest
ProPublica: “When professors moonlight, the income may influence their research and policy views. Although most universities track this outside work, the records have rarely been accessible to the public, potentially obscuring conflicts of interests.
That changed last month when ProPublica launched Dollars for Profs, an interactive database that, for the first time ever, allows you to look up more than 37,000 faculty and staff disclosures from about 20 public universities and the National Institutes of Health.
We believe there are hundreds of stories in this database, and we hope to tell as many as possible. Already, we’ve revealed how the University of California’s weak monitoring of conflicts has allowed faculty members to underreport their outside income, potentially depriving the university of millions of dollars. In addition, using a database of NIH records, we found that health researchers have acknowledged a total of at least $188 million in financial conflicts of interest since 2012.
We hope journalists all over the country will look into the database and find more. Here are tips for local education reporters, college newspaper journalists and anyone else who wants to hold academia accountable on how to dig into the disclosures….(More)”.
Icelandic Citizen Engagement Tool Offers Tips for U.S.
Zack Quaintance at Government Technology: “The world of online discourse was vastly different one decade ago. This was before foreign election meddling, before social media execs were questioned by Congress, and before fighting with cantankerous uncles became an online trope. The world was perhaps more naïve, with a wide-eyed belief in some circles that Internet forums would amplify the voiceless within democracy.
This was the world in which Róbert Bjarnason and his collaborators lived. Based in Iceland, Bjarnason and his team developed a platform in 2010 for digital democracy. It was called Shadow Parliament, and its aim was simply to connect Iceland’s people with its governmental leadership. The platform launched one morning that year, with a comments section for debate. By evening, two users were locked in a deeply personal argument.
“We just looked at each other and thought, this is not going to be too much fun,” Bjarnason recalled recently. “We had just created one more platform for people to argue on.”
Sure, the engagement level was quite high, bringing furious users back to the site repeatedly to launch vitriol, but Shadow Parliament was not fostering the helpful discourse for which it was designed. So, developers scrapped it, pulling from the wreckage lessons to inform future work.
Bjarnason and team, officially a nonprofit called Citizens Foundation, worked for roughly a year, and, eventually, a new platform called Better Reykjavik was born. Better Reykjavik had key differences, chief among them a new debate system with simple tweaks: Citizens must list arguments for and against ideas, and instead of replying to each other directly, they can only down-vote things with which they disagree. This is a design that essentially forces users to create standalone points, rather than volley combative responses at one another, threaded in the fashion of Facebook or Twitter.
“With this framing of it,” Bjarnason said, “we’re not asking people to write the first comment they think of. We’re actually asking people to evaluate the idea.”
One tradeoff is that fury has proven itself to be an incredible driver of traffic, and the site loses that. But what the platform sacrifices in irate engagement, it gains in thoughtful debate. It’s essentially trading anger clicks for coherent discourse, and it’s seen tremendous success within Iceland — where some municipalities report 20 percent citizen usage — as well as throughout the international community, primarily in Europe. All told, Citizens Foundation has now built like-minded projects in 20 countries. And now, it is starting to build platforms for communities in the U.S….(More)”.
The Starving State
Article by Joseph E. Stiglitz, Todd N. Tucker, and Gabriel Zucman at Foreign Affairs: “For millennia, markets have not flourished without the help of the state. Without regulations and government support, the nineteenth-century English cloth-makers and Portuguese winemakers whom the economist David Ricardo made famous in his theory of comparative advantage would have never attained the scale necessary to drive international trade. Most economists rightly emphasize the role of the state in providing public goods and correcting market failures, but they often neglect the history of how markets came into being in the first place. The invisible hand of the market depended on the heavier hand of the state.
The state requires something simple to perform its multiple roles: revenue. It takes money to build roads and ports, to provide education for the young and health care for the sick, to finance the basic research that is the wellspring of all progress, and to staff the bureaucracies that keep societies and economies in motion. No successful market can survive without the underpinnings of a strong, functioning state.
That simple truth is being forgotten today. In the United States, total tax revenues paid to all levels of government shrank by close to four percent of national income over the last two decades, from about 32 percent in 1999 to approximately 28 percent today, a decline unique in modern history among wealthy nations. The direct consequences of this shift are clear: crumbling infrastructure, a slowing pace of innovation, a diminishing rate of growth, booming inequality, shorter life expectancy, and a sense of despair among large parts of the population. These consequences add up to something much larger: a threat to the sustainability of democracy and the global market economy….(More)”.
A Matter of Trust: Higher Education Institutions as Information Fiduciaries in an Age of Educational Data Mining and Learning Analytics
Paper by Kyle M. L. Jones, Alan Rubel and Ellen LeClere: “Higher education institutions are mining and analyzing student data to effect educational, political, and managerial outcomes. Done under the banner of “learning analytics,” this work can—and often does—surface sensitive data and information about, inter alia, a student’s demographics, academic performance, offline and online movements, physical fitness, mental wellbeing, and social network. With these data, institutions and third parties are able to describe student life, predict future behaviors, and intervene to address academic or other barriers to student success (however defined). Learning analytics, consequently, raise serious issues concerning student privacy, autonomy, and the appropriate flow of student data.
We argue that issues around privacy lead to valid questions about the degree to which students should trust their institution to use learning analytics data and other artifacts (algorithms, predictive scores) with their interests in mind. We argue that higher education institutions are paradigms of information fiduciaries. As such, colleges and universities have a special responsibility to their students. In this article, we use the information fiduciary concept to analyze cases when learning analytics violate an institution’s responsibility to its students….(More)”.
One Nation Tracked: An investigation into the smartphone tracking industry
Stuart A. Thompson and Charlie Warzel at the New York Times: “…For brands, following someone’s precise movements is key to understanding the “customer journey” — every step of the process from seeing an ad to buying a product. It’s the Holy Grail of advertising, one marketer said, the complete picture that connects all of our interests and online activity with our real-world actions.
Pointillist location data also has some clear benefits to society. Researchers can use the raw data to provide key insights for transportation studies and government planners. The City Council of Portland, Ore., unanimously approved a deal to study traffic and transit by monitoring millions of cellphones. Unicef announced a plan to use aggregated mobile location data to study epidemics, natural disasters and demographics.
For individual consumers, the value of constant tracking is less tangible. And the lack of transparency from the advertising and tech industries raises still more concerns.
Does a coupon app need to sell second-by-second location data to other companies to be profitable? Does that really justify allowing companies to track millions and potentially expose our private lives?
Data companies say users consent to tracking when they agree to share their location. But those consent screens rarely make clear how the data is being packaged and sold. If companies were clearer about what they were doing with the data, would anyone agree to share it?
What about data collected years ago, before hacks and leaks made privacy a forefront issue? Should it still be used, or should it be deleted for good?
If it’s possible that data stored securely today can easily be hacked, leaked or stolen, is this kind of data worth that risk?
Is all of this surveillance and risk worth it merely so that we can be served slightly more relevant ads? Or so that hedge fund managers can get richer?
The companies profiting from our every move can’t be expected to voluntarily limit their practices. Congress has to step in to protect Americans’ needs as consumers and rights as citizens.
Until then, one thing is certain: We are living in the world’s most advanced surveillance system. This system wasn’t created deliberately. It was built through the interplay of technological advance and the profit motive. It was built to make money. The greatest trick technology companies ever played was persuading society to surveil itself….(More)”.
Accelerating Medicines Partnership (AMP): Improving Drug Research Efficiency through Biomarker Data Sharing
Data Collaborative Case Study by Michelle Winowatan, Andrew Young, and Stefaan Verhulst: “Accelerating Medicines Partnership (AMP) is a cross-sector data-sharing partnership in the United States between the National Institutes of Health (NIH), the Food and Drug Administration (FDA), multiple biopharmaceutical and life science companies, as well as non-profit organizations that seeks to improve the efficiency of developing new diagnostics and treatments for several types of disease. To achieve this goal, the partnership created a pre-competitive collaborative ecosystem where the biomedical community can pool data and resources that are relevant to the prioritized disease areas. A key component of the partnership is to make biomarkers data available to the medical research community through online portals.
Data Collaboratives Model: Based on our typology of data collaborative models, AMP is an example of the data pooling model of data collaboration, specifically a public data pool. Public data pools co-mingle data assets from multiple data holders — in this case pharmaceutical companies — and make those shared assets available on the web. Pools often limit contributions to approved partners (as public data pools are not crowdsourcing efforts), but access to the shared assets is open, enabling independent re-uses.
Data Stewardship Approach: Data stewardship is built into the partnership through the establishment of an executive committee, which governs the entire partnership, and a steering committee for each disease area, which governs each of the sub-projects within AMP. These committees consist of representatives from the institutional partners involved in AMP and perform data stewards function including enabling inter-institutional engagement as well as intra-institutional coordination, data audit and assessment of value and risk, communication of findings, and nurture the collaboration to sustainability….(Full Case Study)”.
Federal Sources of Entrepreneurship Data: A Compendium
Compendium developed by Andrew Reamer: “The E.M. Kauffman Foundation has asked the George Washington Institute of Public Policy (GWIPP) to prepare a compendium of federal sources of data on self-employment, entrepreneurship, and small business development. The Foundation believes that the availability of useful, reliable federal data on these topics would enable robust descriptions and explanations of entrepreneurship trends in the United States and so help guide the development of effective entrepreneurship policies.
Achieving these ends first requires the identification and detailed description of available federal datasets, as provided in this compendium. Its contents include:
- An overview and discussion of 18 datasets from four federal agencies, organized by two categories and five subcategories.
- Tables providing information on each dataset, including:
- scope of coverage of self-employed, entrepreneurs, and businesses;
- data collection methods (nature of data source, periodicity, sampling frame, sample size);
- dataset variables (owner characteristics, business characteristics and operations, geographic areas);
- Data release schedule; and
- Data access by format (including fixed tables, interactive tools, API, FTP download, public use microdata samples [PUMS], and confidential microdata).
For each dataset, examples of studies, if any, that use the data source to describe and explain trends in entrepreneurship.
The author’s aim is for the compendium to facilitate an assessment of the strengths and weaknesses of currently available federal datasets, discussion about how data availability and value can be improved, and implementation of desired improvements…(More)”