Open data governance and open governance: interplay or disconnect?

Curated on Posted on by Stefaan Verhulst

Blog Post by Ana Brandusescu, Carlos Iglesias, Danny Lämmerhirt, and Stefaan Verhulst (in alphabetical order): “The presence of open data often gets listed as an essential requirement toward “open governance”. For instance, an open data strategy is reviewed as a key component of many action plans submitted to the Open Government Partnership. Yet little time is spent on assessing how open data itself is governed, or how it embraces open governance. For example, not much is known on whether the principles and practices that guide the opening up of government — such as transparency, accountability, user-centrism, ‘demand-driven’ design thinking — also guide decision-making on how to release open data.

At the same time, data governance has become more complex and open data decision-makers face heightened concerns with regards to privacy and data protection. The recent implementation of the EU’s General Data Protection Regulation (GDPR) has generated an increased awareness worldwide of the need to prevent and mitigate the risks of personal data disclosures, and that has also affected the open data community. Before opening up data, concerns of data breaches, the abuse of personal information, and the potential of malicious inference from publicly available data may have to be taken into account. In turn, questions of how to sustain existing open data programs, user-centrism, and publishing with purpose gain prominence.

To better understand the practices and challenges of open data governance, we have outlined a research agenda in an earlier blog post. Since then, and perhaps as a result, governance has emerged as an important topic for the open data community. The audience attending the 5th International Open Data Conference (IODC) in Buenos Aires deemed governance of open data to be the most important discussion topic. For instance, discussions around the Open Data Charter principles during and prior to the IODC acknowledged the role of an integrated governance approach to data handling, sharing, and publication. Some conclude that the open data movement has brought about better governance, skills, technologies of public information management which becomes an enormous long-term value for government. But what does open data governance look like?

Understanding open data governance

To expand our earlier exploration and broaden the community that considers open data governance, we convened a workshop at the Open Data Research Symposium 2018. Bringing together open data professionals, civil servants, and researchers, we focused on:

  • What is open data governance?
  • When can we speak of “good” open data governance, and
  • How can the research community help open data decision-makers toward “good” open data governance?

In this session, open data governance was defined as the interplay of rules, standards, tools, principles, processes and decisions that influence what government data is opened up, how and by whom. We then explored multiple layers that can influence open data governance.

In the following, we illustrate possible questions to start mapping the layers of open data governance. As they reflect the experiences of session participants, we see them as starting points for fresh ethnographic and descriptive research on the daily practices of open data governance in governments….(More)”.

Can transparency make extractive industries more accountable?

Curated on Posted on by Stefaan Verhulst

Blog by John Gaventa at IDS: “Over the last two decades great strides have been made in terms of holding extractive industries accountable.  As demonstrated at the Global Assembly of Publish What You Pay (PWYP), which I attended recently in Dakar, Senegal, more information than ever about revenue flows to governments from the oil gas and mining industries is now publicly available.  But new research suggests that such information disclosure, while important, is by itself not enough to hold companies to account, and address corruption.

… a recent study in Mozambique by researchers Nicholas Aworti and Adriano Adriano Nuvunga questions this assumption.  Supported by the Action for Empowerment and Accountability (A4EA) Research Programme, the research explored why greater transparency of information has not necessarily led to greater social and political action for accountability.

Like many countries in Africa, Mozambique is experiencing massive outside investments in recently discovered natural resources, including rich deposits of natural gas and oil, as well as coal and other minerals.  Over the last decade, NGOs like the Centre for Public Integrity, who helped facilitate the study, have done brave and often pioneering work to elicit information on the extractive industry, and to publish it in hard-hitting reports, widely reported in the press, and discussed at high-level stakeholder meetings.

Yet, as Aworti and Nuvunga summarise in a policy brief based on their research, ‘neither these numerous investigative reports nor the EITI validation reports have inspired social and political action such as public protest or state prosecution.’   Corruption continues, and despite the newfound mineral wealth, the country remains one of the poorest in Africa.

The authors ask, ‘If information disclosure has not been enough to galvanise citizen and institutional action, what could be the reason?’ The research found 18 other factors that affect whether information leads to action, including the quality of the information and how it is disseminated, the degree of citizen empowerment, the nature of the political regime, and the role of external donors in insisting on accountability….

The research and the challenges highlighted by the Mozambique case point to the need for new approaches.   At the Global Assembly in Dakar several hundred of PYWP’s more than 700 members from 45 countries gathered to discuss and to approve the organisation’s next strategic plan. Among other points, the plan calls for going beyond transparency –  to more intentionally use information to foster and promote citizen action,  strengthen  grassroots participation and voice on mining issues, and  improve links with other related civil society movements working on gender, climate and tax justice in the extractives field.

Coming at a time where increasing push back and repression threaten the space for citizens to speak truth to power, this is a bold call.  I chaired two sessions with PWYP activists who had been beaten, jailed, threatened or exiled for challenging mining companies, and 70 per cent of the delegates at the conference said their work had been affected by this more repressive environment….(More)”.

Governance of artificial intelligence and personal health information

Curated on Posted on by Stefaan Verhulst

Jenifer Sunrise Winter in Digital Policy, Regulation and Governance: “This paper aims to assess the increasing challenges to governing the personal health information (PHI) essential for advancing artificial intelligence (AI) machine learning innovations in health care. Risks to privacy and justice/equity are discussed, along with potential solutions….

This paper argues that these characteristics of machine learning will overwhelm existing data governance approaches such as privacy regulation and informed consent. Enhanced governance techniques and tools will be required to help preserve the autonomy and rights of individuals to control their PHI. Debate among all stakeholders and informed critique of how, and for whom, PHI-fueled health AI are developed and deployed are needed to channel these innovations in societally beneficial directions.

Health data may be used to address pressing societal concerns, such as operational and system-level improvement, and innovations such as personalized medicine. This paper informs work seeking to harness these resources for societal good amidst many competing value claims and substantial risks for privacy and security….(More).

The Role of Big Data Analytics in Predicting Suicide

Curated on Posted on by Stefaan Verhulst

Chapter by Ronald C. Kessler et al: “…reviews the long history of using electronic medical records and other types of big data to predict suicide. Although a number of the most recent of these studies used machine learning (ML) methods, these studies were all suboptimal both in the features used as predictors and in the analytic approaches used to develop the prediction models. We review these limitations and describe opportunities for making improvements in future applications.

We also review the controversy among clinical experts about using structured suicide risk assessment tools (be they based on ML or older prediction methods) versus in-depth clinical evaluations of needs for treatment planning. Rather than seeing them as competitors, we propose integrating these different approaches to capitalize on their complementary strengths. We also emphasize the distinction between two types of ML analyses: those aimed at predicting which patients are at highest suicide risk, and those aimed at predicting the treatment options that will be best for individual patients. We explain why both are needed to optimize the value of big data ML methods in addressing the suicide problem….(More)”.

See also How Search Engine Data Enhance the Understanding of Determinants of Suicide in India and Inform Prevention: Observational Study.

Rescuing Human Rights: A Radically Moderate Approach

Curated on Posted on by Stefaan Verhulst

Book by Hurst Hannum: “The development of human rights norms is one of the most significant achievements in international relations and law since 1945, but the continuing influence of human rights is increasingly being questioned by authoritarian governments, nationalists, and pundits. Unfortunately, the proliferation of new rights, linking rights to other issues such as international crimes or the activities of business, and attempting to address every social problem from a human rights perspective risk undermining their credibility.

Rescuing Human Rights calls for understanding ‘human rights’ as international human rights law and maintaining the distinctions between binding legal obligations on governments and broader issues of ethics, politics, and social change. Resolving complex social problems requires more than simplistic appeals to rights, and adopting a ‘radically moderate’ approach that recognizes both the potential and the limits of international human rights law, offers the best hope of preserving the principle that we all have rights, simply because we are human….(More)”.

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

Curated on Posted on by Stefaan Verhulst

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 social, economic 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)”

Responsible AI for conservation

Curated on Posted on by Stefaan Verhulst

Oliver Wearn, RobinFreeman and David Jacoby in Nature: “Machine learning (ML) is revolutionizing efforts to conserve nature. ML algorithms are being applied to predict the extinction risk of thousands of species, assess the global footprint of fisheries, and identify animals and humans in wildlife sensor data recorded in the field. These efforts have recently been given a huge boost with support from the commercial sector. New initiatives, such as Microsoft’s AI for Earth and Google’s AI for Social Good, are bringing new resources and new ML tools to bear on some of the biggest challenges in conservation. In parallel to this, the open data revolution means that global-scale, conservation-relevant datasets can be fed directly to ML algorithms from open data repositories, such as Google Earth Engine for satellite data or Movebank for animal tracking data. Added to these will be Wildlife Insights, a Google-supported platform for hosting and analysing wildlife sensor data that launches this year. With new tools and a proliferation of data comes a bounty of new opportunities, but also new responsibilities….(More)”

Weather Service prepares to launch prediction model many forecasters don’t trust

Curated on Posted on by Stefaan Verhulst

Jason Samenow in the Washington Post: “In a month, the National Weather Service plans to launch its “next generation” weather prediction model with the aim of “better, more timely forecasts.” But many meteorologists familiar with the model fear it is unreliable.

The introduction of a model that forecasters lack confidence in matters, considering the enormous impact that weather has on the economy, valued at around $485 billion annually.

The Weather Service announced Wednesday that the model, known as the GFS-FV3 (FV3 stands for Finite­ Volume Cubed-Sphere dynamical core), is “tentatively” set to become the United States’ primary forecast model on March 20, pending tests. It is an update to the current version of the GFS (Global Forecast System), popularly known as the American model, which has existed in various forms for more than 30 years….

A concern is that if forecasters cannot rely on the FV3, they will be left to rely only on the European model for their predictions without a credible alternative for comparisons. And they’ll also have to pay large fees for the European model data. Whereas model data from the Weather Service is free, the European Center for Medium-Range Weather Forecasts, which produces the European model, charges for access.

But there is an alternative perspective, which is that forecasters will just need to adjust to the new model and learn to account for its biases. That is, a little short-term pain is worth the long-term potential benefits as the model improves….

The Weather Service’s parent agency, the National Oceanic and Atmospheric Administration, recently entered an agreement with the National Center for Atmospheric Research to increase collaboration between forecasters and researchers in improving forecast modeling.

In addition, President Trump recently signed into law the Weather Research and Forecast Innovation Act Reauthorization, which establishes the NOAA Earth Prediction Innovation Center, aimed at further enhancing prediction capabilities. But even while NOAA develops relationships and infrastructure to improve the Weather Service’s modeling, the question remains whether the FV3 can meet the forecasting needs of the moment. Until the problems identified are addressed, its introduction could represent a step back in U.S. weather prediction despite a well-intended effort to leap forward….(More).

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

Curated on Posted on by Stefaan Verhulst

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)”.

Democracy Beyond Voting and Protests

Curated on Posted on by Stefaan Verhulst

Sasha Fisher at Project Syndicate: “For over a decade now, we have witnessed more elections and, simultaneously, less democracy. According to Bloomberg, elections have been occurring more frequently around the world. Yet Freedom House finds that some 110 countries have experienced declines in political and civil rights over the past 13 years.

As democracy declines, so does our sense of community. In the United States, this is evidenced by a looming loneliness epidemicand the rapid disappearance of civic institutions such as churches, eight of which close every day. And though these trends are global in nature, the US exemplifies them in the extreme.

This is no coincidence. As Alexis de Tocqueville pointed out in the 1830s, America’s founders envisioned a country governed not by shared values, but by self-interest. That vision has since defined America’s institutions, and fostered a hyper-individualistic society.

Growing distrust in governing institutions has fueled a rise in authoritarian populist movements around the world. Citizens are demanding individual economic security and retreating into an isolationist mentality. ...

And yet we know that “user engagement” works, as shown by countless studies and human experiences. For example, an evaluation conducted in Uganda found that the more citizens participated in the design of health programs, the more the perception of the health-care system improved. And in Indonesia, direct citizen involvement in government decision-making has led to higher satisfaction with government services....

While the Western world suffers from over-individualization, the most notable governance and economic innovations are taking place in the Global South. In Rwanda, for example, the government has introduced policies to encourage grassroots solutions that strengthen citizens’ sense of community and shared accountability. Through monthly community-service meetings, families and individuals work together to build homes for the needy, fix roads, and pool funds to invest in better farming practices and equipment.

Imagine if over 300 million Americans convened every month for a similar purpose. There would suddenly be billions more citizen hours invested in neighbor-to-neighbor interaction and citizen action.

This was one of the main effects of the Village Savings and Loan Associations that originated in the Democratic Republic of Congo. Within communities, members have access to loans to start small businesses and save for a rainy day. The model works because it leverages neighbor-to-neighbor accountability. Likewise, from Haiti to Liberia to Burundi and beyond, community-based health systems have proven effective precisely because health workers know their neighbors and their needs. Community health workers go from home to home, checking in on pregnant mothers and making sure they are cared for. Each of these solutions uses and strengthens communal accountability through shared engagement – not traditional vertical accountability lines.

If we believe in the democratic principle that governments must be accountable to citizens, we should build systems that hold us accountable to each other – and we must engage beyond elections and protests. We must usher in a new era of community-driven democracy – power must be decentralized and placed in the hands of families and communities.

When we achieve community-driven democracy, we will engage with one another and with our governments – not just on special occasions, but continuously, because our democracy and freedom depend on us….(More)” (See also Index on Trust in Institutions)