A New Way of Voting That Makes Zealotry Expensive

Curated on Posted on by Stefaan Verhulst

Peter Coy at Bloomberg Business Week: “An intriguing new tool of democracy just had its first test in the real world of politics, and it passed with flying colors.

The tool is called quadratic voting, and it’s just as nerdy as it sounds. The concept is that each voter is given a certain number of tokens—say, 100—to spend as he or she sees fit on votes for a variety of candidates or issues. Casting one vote for one candidate or issue costs one token, but two votes cost four tokens, three votes cost nine tokens, and so on up to 10 votes costing all 100 of your tokens. In other words, if you really care about one candidate or issue, you can cast up to 10 votes for him, her, or it, but it’s going to cost you all your tokens.

Quadratic voting was invented not by political scientists but by economists and others, including Glen Weyl, an economist and principal researcher at Microsoft Corp. The purpose of quadratic voting is to determine “whether the intense preferences of the minority outweigh the weak preferences of the majority,” Weyl and Eric Posner, a University of Chicago Law School professor, wrote last year in an important book called Radical Markets: Uprooting Capitalism and Democracy for a Just Society. ….

This spring, quadratic voting was used in a successful experiment by the Democratic caucus of the Colorado House of Representatives. The lawmakers used it to decide on their legislative priorities for the coming two years among 107 possible bills. (Wiredmagazine wrote about it here.)…

In this year’s experiment, the 41 lawmakers in the Democratic caucus were given 100 tokens each to allocate among the 107 bills. No one chose to spend all 100 tokens on a single bill. Many of them spread their votes around widely but thinly because it was inexpensive to do so—one vote is just one token. The top vote-getter by a wide margin turned out to be a bill guaranteeing equal pay to women for equal work. “There was clear separation” of the favorites from the also-rans, Hansen says.

The computer interface and other logistics were provided by Democracy Earth, which describes itself as a borderless community and “a global commons of self-sovereign citizens.” The lawmakers had more immediate concerns—hammering out a party agenda. “Some members were more tech-savvy,” Hansen says. “Some started skeptical but came around. I was pleasantly surprised. There was this feeling of ownership—your voice being heard.”

I recently wrote about the democratic benefits of ranked-choice voting, in which voters rank all the candidates in a race and votes are reassigned from the lowest vote-getters to the higher finishers until someone winds up with a majority. But although ranked-choice voting is gaining in popularity, it traces its roots back to the 19th century. Quadratic voting is much more of a break from the past. “This is a new idea, which is rare in economic theory, so it should be saluted as such, especially since it is accompanied by outstanding execution,” George Mason University economist Tyler Cowen wrote in 2015. (He did express some cautions about it as well.)…(More)”.

Grey Data

Curated on Posted on by Michelle Winowatan

/greɪ ˈdeɪtə/

Data accumulated by an institution for operational purposes that does not fall under any traditional data protection policies.

Organizations across all sectors accumulate a massive amount of data just by virtue of operating alone, and universities are among such organizations. In a paper, Christine L. Borgman categorizes these as grey data and further suggested that universities should take a lead in demonstrating stewardship of these data, which include student applications, faculty dossier, registrar records, ID card data, security cameras, and many others.

“Some of these data are collected for mandatory reporting obligations such as enrollments, diversity, budgets, grants, and library collections. Many types of data about individuals are collected for operational and design purposes, whether for instruction, libraries, travel, health, or student services.”

(Borgman, p. 380)

Grey data typically does not fall under traditional data protection policies such as Health Insurance Portability and Accountability Act (HIPAA), Family Educational Rights and Privacy Act (FERPA), or Institutional Review Boards. Consequently, there are a lot of debates about how to use (or misuse) them. Borgman points out that universities have been “exploiting these data for research, learning analytics, faculty evaluation, strategic decisions, and other sensitive matters.” On top of this, for-profit companies “are besieging universities with requests for access to data or for partnerships to mine them.”

Recognizing both the value of data and the risks arising from the accumulation of grey data, Borgman proposes a model of Data Stewardship by drawing on the practices of data protection at the University of California which concern information security, data governance, and cyber risk.

This model is an example of a good Data Stewardship practice that the GovLab is advocating amidst the rise of public-private collaboration in leveraging data for public good.

The GovLab’s Data Stewards website presents the need for such practice as follows:

“With these new practices of data collaborations come the need to reimagine roles and responsibilities to steer the process of using private data, and the insights it can generate, to address some of society’s biggest questions and challenges: Data Stewards.

“Today, establishing and sustaining these new collaborative and accountable approaches requires significant and time-consuming effort and investment of resources for both data holders on the supply side, and institutions that represent the demand. By establishing Data Stewardship as a function, recognized within the private sector as a valued responsibility, the practice of Data Collaboratives can become more predictable, scaleable, sustainable and de-risked.”

Sources and Further Readings:

Nagging misconceptions about nudge theory

Curated on Posted on by Stefaan Verhulst

Cass Sunstein at The Hill: “Nudges are private or public initiatives that steer people in particular directions but that also allow them to go their own way.

A reminder is a nudge; so is a warning. A GPS device nudges; a default rule, automatically enrolling people in some program, is a nudge.

To qualify as a nudge, an initiative must not impose significant economic incentives. A subsidy is not a nudge; a tax is not a nudge; a fine or a jail sentence is not a nudge. To count as such, a nudge must fully preserve freedom of choice.

In 2009, University of Chicago economist Richard Thaler and I co-wrote a book that drew on research in psychology and behavioral economics to help people and institutions, both public and private, improve their decision-making.

In the 10 years since “Nudge” was published, there has been an extraordinary outpouring of new thought and action, with particular reference to public policy.

Behavioral insight teams, or “nudge units” of various sorts, can be found in many nations, including Australia, Canada, Denmark, United Kingdom, the United States, the Netherlands, Germany, Singapore, Japan and Qatar.

Those teams are delivering. By making government more efficient, and by improving safety and health, they are helping to save a lot of money and a lot of lives. And in many countries, including the U.S., they don’t raise partisan hackles; both Democrats and Republicans have enthusiastically embraced them.   

Still, there are a lot of mistakes and misconceptions out there, and they are diverting attention and hence stalling progress. Here are the three big ones:

1. Nudges do not respect freedom. …

2. Nudges are based on excessive trust in government...

3. Nudges cannot achieve a whole lot.…(More)”.

Nowcasting the Local Economy: Using Yelp Data to Measure Economic Activity

Curated on Posted on by Stefaan Verhulst

Paper by Edward L. Glaeser, Hyunjin Kim and Michael Luca: “Can new data sources from online platforms help to measure local economic activity? Government datasets from agencies such as the U.S. Census Bureau provide the standard measures of local economic activity at the local level. However, these statistics typically appear only after multi-year lags, and the public-facing versions are aggregated to the county or ZIP code level. In contrast, crowdsourced data from online platforms such as Yelp are often contemporaneous and geographically finer than official government statistics. Glaeser, Kim, and Luca present evidence that Yelp data can complement government surveys by measuring economic activity in close to real time, at a granular level, and at almost any geographic scale. Changes in the number of businesses and restaurants reviewed on Yelp can predict changes in the number of overall establishments and restaurants in County Business Patterns. An algorithm using contemporaneous and lagged Yelp data can explain 29.2 percent of the residual variance after accounting for lagged CBP data, in a testing sample not used to generate the algorithm. The algorithm is more accurate for denser, wealthier, and more educated ZIP codes….(More)”.

See all papers presented at the NBER Conference on Big Data for 21st Century Economic Statistics here.

Data Pools: Wi-Fi Geolocation Spoofing

Curated on Posted on by Stefaan Verhulst

AH Projects: “DataPools is a Wi-Fi geolocation spoofing project that virtually relocates your phone to the latitudes and longitudes of Silicon Valley success. It includes a catalog and a SkyLift device with 12 pre-programmed locations. DataPools was produced for the Tropez summer art event in Berlin and in collaboration with Anastasia Kubrak.

DataPools catalog pool index

DataPools catalog pool index

Weren’t invited to Jeff Bezos’s summer pool party? No problem. DataPools uses the SkyLift device to mimick the Wi-Fi network infrastructure at 12 of the top Silicon Valley CEOs causing your phone to show up, approximately, at their pool. Because Wi-Fi spoofing affects the core geolocation services of iOS and Android smartphones, all apps on phone and the metadata they generate, will be located in the spoofed location…

Data Pools is a metaphor for a store of wealth that is private. The luxurious pools and mansions of Silicon Valley are financed by the mechanisms of economic surveillance and ownership of our personal information. Yet, the geographic locations of these premises are often concealed, hidden, and removed from open source databases. What if we could reverse this logic and plunge into the pools of ludicrous wealth, both virtually and physically? Could we apply the same methods of data extraction to highlight the ridiculous inequalities between CEOs and platform users?

Comparison of wealth distribution among top Silicon Valley CEOs

Comparison of wealth distribution among top Silicon Valley CEOs

Data

Technically, DataPools uses a Wi-Fi microcontroller programmed with the BSSIDs and SSIDs from the target locations, which were all obtained using openly published information from web searches and wigle.net. This data is then programmed onto the firmware of the SkyLift device. One SkyLift device contains all 12 pool locations. However, throughout the installation improvements were made and the updated firmware now uses one main location with multiple sub-locations to cover a larger area during installations. This method was more effective at spoofing many phones in large area and is ideal for installations….(More)”.

A weather tech startup wants to do forecasts based on cell phone signals

Curated on Posted on by Stefaan Verhulst

Douglas Heaven at MIT Technology Review: “On 14 April more snow fell on Chicago than it had in nearly 40 years. Weather services didn’t see it coming: they forecast one or two inches at worst. But when the late winter snowstorm came it caused widespread disruption, dumping enough snow that airlines had to cancel more than 700 flights across all of the city’s airports.

One airline did better than most, however. Instead of relying on the usual weather forecasts, it listened to ClimaCell – a Boston-based “weather tech” start-up that claims it can predict the weather more accurately than anyone else. According to the company, its correct forecast of the severity of the coming snowstorm allowed the airline to better manage its schedules and minimize losses due to delays and diversions. 

Founded in 2015, ClimaCell has spent the last few years developing the technology and business relationships that allow it to tap into millions of signals from cell phones and other wireless devices around the world. It uses the quality of these signals as a proxy for local weather conditions, such as precipitation and air quality. It also analyzes images from street cameras. It is offering a weather forecasting service to subscribers that it claims is 60 percent more accurate than that of existing providers, such as NOAA.

The internet of weather

The approach makes sense, in principle. Other forecasters use proxies, such as radar signals. But by using information from millions of everyday wireless devices, ClimaCell claims it has a far more fine-grained view of most of the globe than other forecasters get from the existing network of weather sensors, which range from ground-based devices to satellites. (ClimaCell also taps into these, too.)…(More)”.

How Technology Could Revolutionize Refugee Resettlement

Curated on Posted on by Stefaan Verhulst

Krishnadev Calamur in The Atlantic: “… For nearly 70 years, the process of interviewing, allocating, and accepting refugees has gone largely unchanged. In 1951, 145 countries came together in Geneva, Switzerland, to sign the Refugee Convention, the pact that defines who is a refugee, what refugees’ rights are, and what legal obligations states have to protect them.

This process was born of the idealism of the postwar years—an attempt to make certain that those fleeing war or persecution could find safety so that horrific moments in history, such as the Holocaust, didn’t recur. The pact may have been far from perfect, but in successive years, it was a lifeline to Afghans, Bosnians, Kurds, and others displaced by conflict.

The world is a much different place now, though. The rise of populism has brought with it a concomitant hostility toward immigrants in general and refugees in particular. Last October, a gunman who had previously posted anti-Semitic messages online against HIAS killed 11 worshippers in a Pittsburgh synagogue. Many of the policy arguments over resettlement have shifted focus from humanitarian relief to security threats and cost. The Trump administration has drastically cut the number of refugees the United States accepts, and large parts of Europe are following suit.

If it works, Annie could change that dynamic. Developed at Worcester Polytechnic Institute in Massachusetts, Lund University in Sweden, and the University of Oxford in Britain, the software uses what’s known as a matching algorithm to allocate refugees with no ties to the United States to their new homes. (Refugees with ties to the United States are resettled in places where they have family or community support; software isn’t involved in the process.)

Annie’s algorithm is based on a machine learning model in which a computer is fed huge piles of data from past placements, so that the program can refine its future recommendations. The system examines a series of variables—physical ailments, age, levels of education and languages spoken, for example—related to each refugee case. In other words, the software uses previous outcomes and current constraints to recommend where a refugee is most likely to succeed. Every city where HIAS has an office or an affiliate is given a score for each refugee. The higher the score, the better the match.

This is a drastic departure from how refugees are typically resettled. Each week, HIAS and the eight other agencies that allocate refugees in the United States make their decisions based largely on local capacity, with limited emphasis on individual characteristics or needs….(More)”.

How to Argue with an Algorithm: Lessons from the COMPAS ProPublica Debate

Curated on Posted on by Stefaan Verhulst

Paper by Anne L. Washington: “The United States optimizes the efficiency of its growing criminal justice system with algorithms however, legal scholars have overlooked how to frame courtroom debates about algorithmic predictions. In State v Loomis, the defense argued that the court’s consideration of risk assessments during sentencing was a violation of due process because the accuracy of the algorithmic prediction could not be verified. The Wisconsin Supreme Court upheld the consideration of predictive risk at sentencing because the assessment was disclosed and the defendant could challenge the prediction by verifying the accuracy of data fed into the algorithm.

Was the court correct about how to argue with an algorithm?

The Loomis court ignored the computational procedures that processed the data within the algorithm. How algorithms calculate data is equally as important as the quality of the data calculated. The arguments in Loomis revealed a need for new forms of reasoning to justify the logic of evidence-based tools. A “data science reasoning” could provide ways to dispute the integrity of predictive algorithms with arguments grounded in how the technology works.

This article’s contribution is a series of arguments that could support due process claims concerning predictive algorithms, specifically the Correctional Offender Management Profiling for Alternative Sanctions (“COMPAS”) risk assessment. As a comprehensive treatment, this article outlines the due process arguments in Loomis, analyzes arguments in an ongoing academic debate about COMPAS, and proposes alternative arguments based on the algorithm’s organizational context….(More)”

Trust, Control, and the Economics of Governance

Curated on Posted on by Stefaan Verhulst

Book by Philipp Herold: “In today’s world, we cooperate across legal and cultural systems in order to create value. However, this increases volatility, uncertainty, complexity, and ambiguity as challenges for societies, politics, and business. This has made governance a scarce resource. It thus is inevitable that we understand the means of governance available to us and are able to economize on them. Trends like the increasing role of product labels and a certification industry as well as political movements towards nationalism and conservatism may be seen as reaction to disappointments from excessive cooperation. To avoid failures of cooperation, governance is important – control through e.g. contracts is limited and in governance economics trust is widely advertised without much guidance on its preconditions or limits.

This book draws on the rich insight from research on trust and control, and accommodates the key results for governance considerations in an institutional economics framework. It provides a view on the limits of cooperation from the required degree of governance, which can be achieved through extrinsic motivation or building on intrinsic motivation. Trust Control Economics thus inform a more realistic expectation about the net value added from cooperation by providing a balanced view including the cost of governance. It then becomes clear how complex cooperation is about ‘governance accretion’ where limited trustworthiness is substituted by control and these control instances need to be governed in turn.

Trust, Control, and the Economics of Governance is a highly necessary development of institutional economics to reflect progress made in trust research and is a relevant addition for practitioners to better understand the role of trust in the governance of contemporary cooperation-structures. It will be of interest to researchers, academics, and students in the fields of economics and business management, institutional economics, and business ethics….(More)”.

Report on Algorithmic Risk Assessment Tools in the U.S. Criminal Justice System

Curated on Posted on by Stefaan Verhulst

Press release: “The Partnership on AI (PAI) has today published a report gathering the views of the multidisciplinary artificial intelligence and machine learning research and ethics community which documents the serious shortcomings of algorithmic risk assessment tools in the U.S. criminal justice system. These kinds of AI tools for deciding on whether to detain or release defendants are in widespread use around the United States, and some legislatures have begun to mandate their use. Lessons drawn from the U.S. context have widespread applicability in other jurisdictions, too, as the international policymaking community considers the deployment of similar tools.

While criminal justice risk assessment tools are often simpler than the deep neural networks used in many modern artificial intelligence systems, they are basic forms of AI. As such, they present a paradigmatic example of the high-stakes social and ethical consequences of automated AI decision-making….

Across the report, challenges to using these tools fell broadly into three primary categories:

  1. Concerns about the accuracy, bias, and validity in the tools themselves
    • Although the use of these tools is in part motivated by the desire to mitigate existing human fallibility in the criminal justice system, this report suggests that it is a serious misunderstanding to view tools as objective or neutral simply because they are based on data.
  2. Issues with the interface between the tools and the humans who interact with them
    • In addition to technical concerns, these tools must be held to high standards of interpretability and explainability to ensure that users (including judges, lawyers, and clerks, among others) can understand how the tools’ predictions are reached and make reasonable decisions based on these predictions.
  3. Questions of governance, transparency, and accountability
    • To the extent that such systems are adapted to make life-changing decisions, tools and decision-makers who specify, mandate, and deploy them must meet high standards of transparency and accountability.

This report highlights some of the key challenges with the use of risk assessment tools for criminal justice applications. It also raises some deep philosophical and procedural issues which may not be easy to resolve. Surfacing and addressing those concerns will require ongoing research and collaboration between policymakers, the AI research community, civil society groups, and affected communities, as well as new types of data collection and transparency. It is PAI’s mission to spur and facilitate these conversations and to produce research to bridge such gaps….(More)”