Belgium’s democratic experiment


David van Reybrouck in Politico: “Those looking for a solution to the wave of anger and distrust sweeping Western democracies should have a look at an experiment in European democracy taking place in a small region in eastern Belgium.

Starting in September, the parliament representing the German-speaking region of Belgium will hand some of its powers to a citizens’ assembly drafted by lot. It’ll be the first time a political institution creates a permanent structure to involve citizens in political decision making.

It’s a move Belgian media has rightly hailed as “historic.” I was in parliament the night MPs from all six parties moved past ideological differences to endorse the bill. It was a courageous move, a sign to other politicians — who tend to see their voters as a threat rather than a resource — that citizens should be trusted, not feared, or “spun.”

Nowhere else in the world will everyday citizens be so consistently involved in shaping the future of their community. In times of massive, widespread distrust of party politics, German-speaking Belgians will be empowered to put the issues they care about on the agenda, to discuss potential solutions, and to monitor the follow-up of their recommendations as they pass through parliament and government. Politicians, in turn, will be able to tap independent citizens’ panels to deliberate over thorny political issues.

This experiment is happening on a small scale: Belgium’s German-speaking community, the country’s third linguistic region, is the smallest federal entity in Europe. But its powers are comparable with those of Scotland or the German province of North Rhine-Westphalia, and the lessons of its experiment with a “people’s senate” will have implications for democrats across Europe….(More)”.

A New Way of Voting That Makes Zealotry Expensive


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)”.

Introducing the Contractual Wheel of Data Collaboration


Blog by Andrew Young and Stefaan Verhulst: “Earlier this year we launched the Contracts for Data Collaboration (C4DC) initiative — an open collaborative with charter members from The GovLab, UN SDSN Thematic Research Network on Data and Statistics (TReNDS), University of Washington and the World Economic Forum. C4DC seeks to address the inefficiencies of developing contractual agreements for public-private data collaboration by informing and guiding those seeking to establish a data collaborative by developing and making available a shared repository of relevant contractual clauses taken from existing legal agreements. Today TReNDS published “Partnerships Founded on Trust,” a brief capturing some initial findings from the C4DC initiative.

The Contractual Wheel of Data Collaboration [beta]

The Contractual Wheel of Data Collaboration [beta] — Stefaan G. Verhulst and Andrew Young, The GovLab

As part of the C4DC effort, and to support Data Stewards in the private sector and decision-makers in the public and civil sectors seeking to establish Data Collaboratives, The GovLab developed the Contractual Wheel of Data Collaboration [beta]. The Wheel seeks to capture key elements involved in data collaboration while demystifying contracts and moving beyond the type of legalese that can create confusion and barriers to experimentation.

The Wheel was developed based on an assessment of existing legal agreements, engagement with The GovLab-facilitated Data Stewards Network, and analysis of the key elements of our Data Collaboratives Methodology. It features 22 legal considerations organized across 6 operational categories that can act as a checklist for the development of a legal agreement between parties participating in a Data Collaborative:…(More)”.

Access to Algorithms


Paper by Hannah Bloch-Wehba: “Federal, state, and local governments increasingly depend on automated systems — often procured from the private sector — to make key decisions about civil rights and civil liberties. When individuals affected by these decisions seek access to information about the algorithmic methodologies that produced them, governments frequently assert that this information is proprietary and cannot be disclosed. 

Recognizing that opaque algorithmic governance poses a threat to civil rights and liberties, scholars have called for a renewed focus on transparency and accountability for automated decision making. But scholars have neglected a critical avenue for promoting public accountability and transparency for automated decision making: the law of access to government records and proceedings. This Article fills this gap in the literature, recognizing that the Freedom of Information Act, its state equivalents, and the First Amendment provide unappreciated legal support for algorithmic transparency.

The law of access performs three critical functions in promoting algorithmic accountability and transparency. First, by enabling any individual to challenge algorithmic opacity in government records and proceedings, the law of access can relieve some of the burden otherwise borne by parties who are often poor and under-resourced. Second, access law calls into question government’s procurement of algorithmic decision making technologies from private vendors, subject to contracts that include sweeping protections for trade secrets and intellectual property rights. Finally, the law of access can promote an urgently needed public debate on algorithmic governance in the public sector….(More)”.

Big data needs big governance: best practices from Brain-CODE, the Ontario Brain Institute’s neuroinformatics platform


Shannon C. Lefaivre et al in Frontiers of Genetics: “The Ontario Brain Institute (OBI) has begun to catalyze scientific discovery in the field of neuroscience through its large-scale informatics platform, known as Brain-CODE. The platform supports the capture, storage, federation, sharing and analysis of different data types across several brain disorders. Underlying the platform is a robust and scalable data governance structure which allows for the flexibility to advance scientific understanding, while protecting the privacy of research participants.

Recognizing the value of an open science approach to enabling discovery, the governance structure was designed not only to support collaborative research programs, but also to support open science by making all data open and accessible in the future. OBI’s rigorous approach to data sharing maintains the accessibility of research data for big discoveries without compromising privacy and security. Taking a Privacy by Design approach to both data sharing and development of the platform has allowed OBI to establish some best practices related to large scale data sharing within Canada. The aim of this report is to highlight these best practices and develop a key open resource which may be referenced during the development of similar open science initiatives….(More)”.

Using Data Sharing Agreements as Tools of Indigenous Data Governance: Current Uses and Future Options


Paper by Martinez, A. and Rainie, S. C.: “Indigenous communities and scholars have been influencing a shift in participation and inclusion in academic and agency research over the past two decades. As a response, Indigenous peoples are increasingly asking research questions and developing their own studies rooted in their cultural values. They use the study results to rebuild their communities and to protect their lands. This process of Indigenous-driven research has led to partnering with academic institutions, establishing research review boards, and entering into data sharing agreements to protect environmental data, community information, and local and traditional knowledges.

Data sharing agreements provide insight into how Indigenous nations are addressing the key areas of data collection, ownership, application, storage, and the potential for data reuse in the future. By understanding this mainstream data governance mechanism, how they have been applied, and how they have been used in the past, we aim to describe how Indigenous nations and communities negotiate data protection and control with researchers.

The project described here reviewed publicly available data sharing agreements that focus on research with Indigenous nations and communities in the United States. We utilized qualitative analysis methods to identify specific areas of focus in the data sharing agreements, whether or not traditional or cultural values were included in the language of the data sharing agreements, and how the agreements defined data. The results detail how Indigenous peoples currently use data sharing agreements and potential areas of expansion for language to include in data sharing agreements as Indigenous peoples address the research needs of their communities and the protection of community and cultural data….(More)”.

Shutting down the internet doesn’t work – but governments keep doing it


George Ogola in The Conversation: “As the internet continues to gain considerable power and agency around the world, many governments have moved to regulate it. And where regulation fails, some states resort to internet shutdowns or deliberate disruptions.

The statistics are staggering. In India alone, there were 154 internet shutdowns between January 2016 and May 2018. This is the most of any country in the world.

But similar shutdowns are becoming common on the African continent. Already in 2019 there have been shutdowns in Cameroon, the Democratic Republic of Congo, Republic of Congo, Chad, Sudan and Zimbabwe. Last year there were 21 such shutdowns on the continent. This was the case in Togo, Sierra Leone, Sudan and Ethiopia, among others.

The justifications for such shutdowns are usually relatively predictable. Governments often claim that internet access is blocked in the interest of public security and order. In some instances, however, their reasoning borders on the curious if not downright absurd, like the case of Ethiopia in 2017 and Algeria in 2018 when the internet was shut down apparently to curb cheating in national examinations.

Whatever their reasons, governments have three general approaches to controlling citzens’ access to the web.

How they do it

Internet shutdowns or disruptions usually take three forms. The first and probably the most serious is where the state completely blocks access to the internet on all platforms. It’s arguably the most punitive, with significant socialeconomic and political costs.

The financial costs can run into millions of dollars for each day the internet is blocked. A Deloitte report on the issue estimates that a country with average connectivity could lose at least 1.9% of its daily GDP for each day all internet services are shut down.

For countries with average to medium level connectivity the loss is 1% of daily GDP, and for countries with average to low connectivity it’s 0.4%. It’s estimated that Ethiopia, for example, could lose up to US$500,000 a day whenever there is a shutdown. These shutdowns, then, damage businesses, discourage investments, and hinder economic growth.

The second way that governments restrict internet access is by applying content blocking techniques. They restrict access to particular sites or applications. This is the most common strategy and it’s usually targeted at social media platforms. The idea is to stop or limit conversations on these platforms.

Online spaces have become the platform for various forms of political expression that many states especially those with authoritarian leanings consider subversive. Governments argue, for example, that social media platforms encourage the spread of rumours which can trigger public unrest.

This was the case in 2016 in Uganda during the country’s presidential elections. The government restricted access to social media, describing the shutdown as a “security measure to avert lies … intended to incite violence and illegal declaration of election results”.

In Zimbabwe, the government blocked social media following demonstrations over an increase in fuel prices. It argued that the January 2019 ban was because the platforms were being “used to coordinate the violence”.

The third strategy, done almost by stealth, is the use of what is generally known as “bandwidth throttling”. In this case telecom operators or internet service providers are forced to lower the quality of their cell signals or internet speed. This makes the internet too slow to use. “Throttling” can also target particular online destinations such as social media sites….(More)”

Dirty Data, Bad Predictions: How Civil Rights Violations Impact Police Data, Predictive Policing Systems, and Justice


Paper by Rashida Richardson, Jason Schultz, and Kate Crawford: “Law enforcement agencies are increasingly using algorithmic predictive policing systems to forecast criminal activity and allocate police resources. Yet in numerous jurisdictions, these systems are built on data produced within the context of flawed, racially fraught and sometimes unlawful practices (‘dirty policing’). This can include systemic data manipulation, falsifying police reports, unlawful use of force, planted evidence, and unconstitutional searches. These policing practices shape the environment and the methodology by which data is created, which leads to inaccuracies, skews, and forms of systemic bias embedded in the data (‘dirty data’). Predictive policing systems informed by such data cannot escape the legacy of unlawful or biased policing practices that they are built on. Nor do claims by predictive policing vendors that these systems provide greater objectivity, transparency, or accountability hold up. While some systems offer the ability to see the algorithms used and even occasionally access to the data itself, there is no evidence to suggest that vendors independently or adequately assess the impact that unlawful and bias policing practices have on their systems, or otherwise assess how broader societal biases may affect their systems.

In our research, we examine the implications of using dirty data with predictive policing, and look at jurisdictions that (1) have utilized predictive policing systems and (2) have done so while under government commission investigations or federal court monitored settlements, consent decrees, or memoranda of agreement stemming from corrupt, racially biased, or otherwise illegal policing practices. In particular, we examine the link between unlawful and biased police practices and the data used to train or implement these systems across thirteen case studies. We highlight three of these: (1) Chicago, an example of where dirty data was ingested directly into the city’s predictive system; (2) New Orleans, an example where the extensive evidence of dirty policing practices suggests an extremely high risk that dirty data was or will be used in any predictive policing application, and (3) Maricopa County where despite extensive evidence of dirty policing practices, lack of transparency and public accountability surrounding predictive policing inhibits the public from assessing the risks of dirty data within such systems. The implications of these findings have widespread ramifications for predictive policing writ large. Deploying predictive policing systems in jurisdictions with extensive histories of unlawful police practices presents elevated risks that dirty data will lead to flawed, biased, and unlawful predictions which in turn risk perpetuating additional harm via feedback loops throughout the criminal justice system. Thus, for any jurisdiction where police have been found to engage in such practices, the use of predictive policing in any context must be treated with skepticism and mechanisms for the public to examine and reject such systems are imperative….(More)”.

Fact-Based Policy: How Do State and Local Governments Accomplish It?


Report and Proposal by Justine Hastings: “Fact-based policy is essential to making government more effective and more efficient, and many states could benefit from more extensive use of data and evidence when making policy. Private companies have taken advantage of declining computing costs and vast data resources to solve problems in a fact-based way, but state and local governments have not made as much progress….

Drawing on her experience in Rhode Island, Hastings proposes that states build secure, comprehensive, integrated databases, and that they transform those databases into data lakes that are optimized for developing insights. Policymakers can then use the insights from this work to sharpen policy goals, create policy solutions, and measure progress against those goals. Policymakers, computer scientists, engineers, and economists will work together to build the data lake and analyze the data to generate policy insights….(More)”.

Saying yes to State Longitudinal Data Systems: building and maintaining cross agency relationships


Report by the National Skills Coalition: “In order to provide actionable information to stakeholders, state longitudinal data systems use administrative data that state agencies collect through administering programs. Thus, state longitudinal data systems must maintain strong working relationships with the state agencies collecting necessary administrative data. These state agencies can include K-12 and higher education agencies, workforce agencies, and those administering social service programs such as the Supplemental Nutrition Assistance Program or Temporary Assistance for Needy Families.

When state longitudinal data systems have strong relationships with agencies, agencies willingly and promptly share their data with the system, engage with data governance when needed, approve research requests in a timely manner, and continue to cooperate with the system over the long term. If state agencies do not participate with their state’s longitudinal data system, the work of the system is put into jeopardy. States may find that research and performance reporting can be stalled or stopped outright.

Kentucky and Virginia have been able to build and maintain support for their systems among state agencies. Their example demonstrates how states can effectively utilize their state longitudinal data systems….(More)”.