Introducing the (World’s First) Ethical Operating System

Curated on Posted on by Stefaan Verhulst

Article by Paula Goldman and Raina Kumra: “Is it possible for tech developers to anticipate future risks? Or are these future risks so unknowable to us here in the present that, try as we might to make our tech safe, continued exposure to risks is simply the cost of engagement?

 Today, in collaboration with Institute for the Future (IFTF), a leading non-profit strategic futures organization, Omidyar Network is excited to introduce the Ethical Operating System (or Ethical OS for short), a toolkit for helping developers and designers anticipate the future impact of technologies they’re working on today. We designed the Ethical OS to facilitate better product development, faster deployment, and more impactful innovation — all while striving to minimize technical and reputational risks. The hope is that, with the Ethical OS in hand, technologists can begin to build responsibility into core business and product decisions, and contribute to a thriving tech industry.

The Ethical OS is already being piloted by nearly 20 tech companies, schools, and startups, including Mozilla and Techstars. We believe it can better equip technologists to grapple with three of the most pressing issues facing our community today:

    • If the technology you’re building right now will someday be used in unexpected ways, how can you hope to be prepared?

 

    • What new categories of risk should you pay special attention to right now?

 

  • Which design, team, or business model choices can actively safeguard users, communities, society, and your company from future risk?

As large sections of the public grow weary of a seemingly constant stream of data safety and security issues, and with growing calls for heightened government intervention and oversight, the time is now for the tech community to get this right.

We created the Ethical OS as a pilot to help make ethical thinking and future risk mitigation integral components of all design and development processes. It’s not going to be easy. The industry has far more work to do, both inside individual companies and collectively. But with our toolkit as a guide, developers will have a practical means of helping to begin working to ensure their tech is as good as their intentions…(More)”.

China’s Aggressive Surveillance Technology Will Spread Beyond Its Borders

Curated on Posted on by Stefaan Verhulst

Daniel Benaim and Hollie Russon Gilman at Slate: “…What happens when these technologies migrate to bigger, more fractious societies? This won’t happen overnight—and the financial and logistical obstacles to broad implementation are significant. But there is every reason to think that in a decade or two, if not sooner, authoritarians and would-be strongmen in places like Turkey, Hungary, Egypt, or Rwanda will seek these tools and use them to thwart civil society and crush dissent in ways that weaken democracy globally.

Already there are reports that Zimbabwe, for example, is turning to Chinese firms to implement nationwide facial-recognition and surveillance programs, wrapped into China’s infrastructure investments and a larger set of security agreements as well, including for policing online communication. The acquisition of black African faces will help China’s tech sector improve its overall data set.

Malaysia, too, announced new partnerships this spring with China to equip police with wearable facial-recognition cameras. There are quiet reports of Arab Gulf countries turning to China not just for the drone technologies America has denied but also for the authoritarian suite of surveillance, recognition, and data tools perfected in China’s provinces. In a recent article on Egypt’s military-led efforts to build a new capital city beyond Cairo’s chaos and revolutionary squares, a retired general acting as project spokesman declared, “a smart city means a safe city, with cameras and sensors everywhere. There will be a command center to control the entire city.” Who is financing construction? China.

While many governments are making attempts to secure this information, there have been several alarming stories of data leaks. Moreover, these national identifiers create an unprecedented opportunity for state surveillance at scale. What about collecting biometric information in nondemocratic regimes? In 2016, the personal details of nearly 50 million people in Turkey were leaked….

China and other determined authoritarian states may prove undeterrable in their zeal to adopt repressive technologies. A more realistic goal, as Georgetown University scholar Nicholas Wright has argued, is to sway countries on the fence by pointing out the reputational costs of repression and supporting those who are advocating for civil liberties in this domain within their own countries. Democracy promoters (which we hope will one day again include the White House) will also want to recognize the coming changes to the authoritarian public sphere. They can start now in helping vulnerable populations and civil society to gain greater technological literacy to advocate for their rights in new domains. It is not too early for governments and civil society groups alike to study what technological and tactical countermeasures exist to circumvent and disrupt new authoritarian tools.

Seven years ago, techno-optimists expressed hope that a wave of new digital tools for social networking and self-expression could help young people in the Middle East and elsewhere to find their voices. Today, a new wave of Chinese-led technological advances threatens to blossom into what we consider an “Arab spring in reverse”—in which the next digital wave shifts the pendulum back, enabling state domination and repression at a staggering scale and algorithmic effectiveness.

Americans are absolutely right to be urgently focused on countering Russian weaponized hacking and leaking as its primary beneficiary sits in the Oval Office. But we also need to be more proactive in countering the tools of algorithmic authoritarianism that will shape the worldwide future of individual freedom….(More)”.

Mapping the Privacy-Utility Tradeoff in Mobile Phone Data for Development

Curated on Posted on by Stefaan Verhulst

Paper by Alejandro Noriega-Campero, Alex Rutherford, Oren Lederman, Yves A. de Montjoye, and Alex Pentland: “Today’s age of data holds high potential to enhance the way we pursue and monitor progress in the fields of development and humanitarian action. We study the relation between data utility and privacy risk in large-scale behavioral data, focusing on mobile phone metadata as paradigmatic domain. To measure utility, we survey experts about the value of mobile phone metadata at various spatial and temporal granularity levels. To measure privacy, we propose a formal and intuitive measure of reidentification risk—the information ratio—and compute it at each granularity level. Our results confirm the existence of a stark tradeoff between data utility and reidentifiability, where the most valuable datasets are also most prone to reidentification. When data is specified at ZIP-code and hourly levels, outside knowledge of only 7% of a person’s data suffices for reidentification and retrieval of the remaining 93%. In contrast, in the least valuable dataset, specified at municipality and daily levels, reidentification requires on average outside knowledge of 51%, or 31 data points, of a person’s data to retrieve the remaining 49%. Overall, our findings show that coarsening data directly erodes its value, and highlight the need for using data-coarsening, not as stand-alone mechanism, but in combination with data-sharing models that provide adjustable degrees of accountability and security….(More)”.

A roadmap for restoring trust in Big Data

Curated on Posted on by Stefaan Verhulst
Mark Lawler et al in the Lancet: “The fallout from the Cambridge Analytica–Facebook scandal marks a significant inflection point in the public’s trust concerning Big Data. The health-science community must use this crisis-in-confidence to redouble its commitment to talk openly and transparently about benefits and risks and to act decisively to deliver robust effective governance frameworks, under which personal health data can be responsibly used. Activities such as the Innovative Medicines Initiative’s Big Data for Better Outcomes emphasise how a more granular data-driven understanding of human diseases including cancer could underpin innovative therapeutic intervention.
 Health Data Research UK is developing national research expertise and infrastructure to maximise the value of health data science for the National Health Service and ultimately British citizens.
Comprehensive data analytics are crucial to national programmes such as the US Cancer Moonshot, the UK’s 100 000 Genomes project, and other national genomics programmes. Cancer Core Europe, a research partnership between seven leading European oncology centres, has personal data sharing at its core. The Global Alliance for Genomics and Health recently highlighted the need for a global cancer knowledge network to drive evidence-based solutions for a disease that kills more than 8·7 million citizens annually worldwide. These activities risk being fatally undermined by the recent data-harvesting controversy.
We need to restore the public’s trust in data science and emphasise its positive contribution in addressing global health and societal challenges. An opportunity to affirm the value of data science in Europe was afforded by Digital Day 2018, which took place on April 10, 2018, in Brussels, and where European Health Ministers signed a declaration of support to link existing or future genomic databanks across the EU, through the Million European Genomes Alliance.
So how do we address evolving challenges in analysis, sharing, and storage of information, ensure transparency and confidentiality, and restore public trust? We must articulate a clear Social Contract, where citizens (as data donors) are at the heart of decision-making. We need to demonstrate integrity, honesty, and transparency as to what happens to data and what level of control people can, or cannot, expect. We must embed ethical rigour in all our data-driven processes. The Framework for Responsible Sharing of Genomic and Health Related Data represents a practical global approach, promoting effective and ethical sharing and use of research or patient data, while safeguarding individual privacy through secure and accountable data transfer…(More)”.

Americans Want to Share Their Medical Data. So Why Can’t They?

Curated on Posted on by Stefaan Verhulst

Eleni Manis at RealClearHealth: “Americans are willing to share personal data — even sensitive medical data — to advance the common good. A recent Stanford University study found that 93 percent of medical trial participants in the United States are willing to share their medical data with university scientists and 82 percent are willing to share with scientists at for-profit companies. In contrast, less than a third are concerned that their data might be stolen or used for marketing purposes.

However, the majority of regulations surrounding medical data focus on individuals’ ability to restrict the use of their medical data, with scant attention paid to supporting the ability to share personal data for the common good. Policymakers can begin to right this balance by establishing a national medical data donor registry that lets individuals contribute their medical data to support research after their deaths. Doing so would help medical researchers pursue cures and improve health care outcomes for all Americans.

Increased medical data sharing facilitates advances in medical science in three key ways. First, de-identified participant-level data can be used to understand the results of trials, enabling researchers to better explicate the relationship between treatments and outcomes. Second, researchers can use shared data to verify studies and identify cases of data fraud and research misconduct in the medical community. For example, one researcher recently discovered a prolific Japanese anesthesiologist had falsified data for almost two decades. Third, shared data can be combined and supplemented to support new studies and discoveries.

Despite these benefits, researchers, research funders, and regulators have struggled to establish a norm for sharing clinical research data. In some cases, regulatory obstacles are to blame. HIPAA — the federal law regulating medical data — blocks some sharing on grounds of patient privacy, while federal and state regulations governing data sharing are inconsistent. Researchers themselves have a proprietary interest in data they produce, while academic researchers seeking to maximize publications may guard data jealously.

Though funding bodies are aware of this tension, they are unable to resolve it on their own. The National Institutes of Health, for example, requires a data sharing plan for big-ticket funding but recognizes that proprietary interests may make sharing impossible….(More)”.

#TrendingLaws: How can Machine Learning and Network Analysis help us identify the “influencers” of Constitutions?

Curated on Posted on by Stefaan Verhulst
Unicef: “New research by scientists from UNICEF’s Office of Innovation — published today in the journal Nature Human Behaviour — applies methods from network science and machine learning to constitutional law.  UNICEF Innovation Data Scientists Alex Rutherford and Manuel Garcia-Herranz collaborated with computer scientists and political scientists at MIT, George Washington University, and UC Merced to apply data analysis to the world’s constitutions over the last 300 years. This work sheds new light on how to better understand why countries’ laws change and incorporate social rights…

Data science techniques allow us to use methods like network science and machine learning to uncover patterns and insights that are hard for humans to see. Just as we can map influential users on Twitter — and patterns of relations between places to predict how diseases will spread — we can identify which countries have influenced each other in the past and what are the relations between legal provisions.

Why The Science of Constitutions?

One way UNICEF fulfills its mission is through advocacy with national governments — to enshrine rights for minorities, notably children, formally in law. Perhaps the most renowned example of this is the International Convention on the Rights of the Child (ICRC).

Constitutions, such as Mexico’s 1917 constitution — the first to limit the employment of children — are critical to formalizing rights for vulnerable populations. National constitutions describe the role of a country’s institutions, its character in the eyes of the world, as well as the rights of its citizens.

From a scientific standpoint, the work is an important first step in showing that network analysis and machine learning technique can be used to better understand the dynamics of caring for and protecting the rights of children — critical to the work we do in a complex and interconnected world. It shows the significant, and positive policy implications of using data science to uphold children’s rights.

What the Research Shows:

Through this research, we uncovered:

  • A network of relationships between countries and their constitutions.
  • A natural progression of laws — where fundamental rights are a necessary precursor to more specific rights for minorities.
  • The effect of key historical events in changing legal norms….(More)”.

The Government-Citizen Disconnect

Curated on Posted on by Stefaan Verhulst

Book by Suzanne Mettler: “Americans’ relationship to the federal government is paradoxical. Polls show that public opinion regarding the government has plummeted to all-time lows, with only one in five saying they trust the government or believe that it operates in their interest. Yet, at the same time, more Americans than ever benefit from some form of government social provision. Political scientist Suzanne Mettler calls this growing gulf between people’s perceptions of government and the actual role it plays in their lives the “government-citizen disconnect.” In The Government-Citizen Disconnect, she explores the rise of this phenomenon and its implications for policymaking and politics.

Drawing from original survey data which probed Americans’ experiences of 21 federal social policies — such as food stamps, Social Security, Medicaid, and the home mortgage interest deduction — Mettler shows that 96 percent of adults have received benefits from at least one of them, and that the average person has utilized five. Overall usage rates transcend social, economic, and political divisions, and most Americans report positive experiences of their policy experiences. However, the fact that they have benefited from these policies bears little positive effect on people’s attitudes towards government. Mettler finds that shared identities and group affiliations are more powerful and consistent influences. In particular, those who oppose welfare tend to extrapolate their unfavorable views of it to government in general. Deep antipathy toward the government has emerged as a conservative movement waged a war on social welfare policies for over forty years, even as economic inequality and benefit use increased.

Mettler finds that patterns of political participation exacerbate the government-citizen disconnect, as those holding positive views of federal programs and supporting expanded benefits have lower rates of involvement than those holding more hostile views of the government. As a result, the loudest political voice belongs to those who have benefited from policies but who give government little credit for their economic well-being, seeing their success more as a matter of their own deservingness. This contributes to the election of politicians who advocate cutting federal social programs. According to Mettler, the government-citizen disconnect frays the bonds of representative government and democracy.

The Government-Citizen Disconnect illuminates a paradox that increasingly shapes American politics. Mettler’s examination of hostility toward government at a time when most Americans will at some point rely on the social benefits it provides helps us better understand the roots of today’s fractious political climate….(More)”

Satellites can advance sustainable development by highlighting poverty

Curated on Posted on by Stefaan Verhulst

Cordis: “Estimating poverty is crucial for improving policymaking and advancing the sustainability of a society. Traditional poverty estimation methods such as household surveys and census data incur huge costs however, creating a need for more efficient approaches.

With this in mind, the EU-funded USES project examined how satellite images could be used to estimate household-level poverty in rural regions of developing countries. “This promises to be a radically more cost-effective way of monitoring and evaluating the Sustainable Development Goals,” says Dr Gary Watmough, USES collaborator and Interdisciplinary Lecturer in Land Use and Socioecological Systems at the University of Edinburgh, United Kingdom.

Land use and land cover reveal poverty clues

To achieve its aims, the project investigated how land use and land cover information from satellite data could be linked with household survey data. “We looked particularly at how households use the landscape in the local area for agriculture and other purposes such as collecting firewood and using open areas for grazing cattle,” explains Dr Watmough.

The work also involved examining satellite images to determine which types of land use were related to household wealth or poverty using statistical analysis. “By trying to predict household poverty using the land use data we could see which land use variables were most related to the household wealth in the area,” adds Dr Watmough.

Overall, the USES project found that satellite data could predict poverty particularly the poorest households in the area. Dr Watmough comments: “This is quite remarkable given that we are trying to predict complicated household-level poverty from a simple land use map derived from high-resolution satellite data.”

A study conducted by USES in Kenya found that the most important remotely sensed variable was building size within the homestead. Buildings less than 140 m2 were mostly associated with poorer households, whereas those over 140 m2 tended to be wealthier. The amount of bare ground in agricultural fields and within the homestead region was also important. “We also found that poorer households were associated with a shorter number of agricultural growing days,” says Dr Watmough….(More)”.

The Democratization of Data Science

Curated on Posted on by Stefaan Verhulst

Jonathan Cornelissen at Harvard Business School: “Want to catch tax cheats? The government of Rwanda does — and it’s finding them by studying anomalies in revenue-collection data.

Want to understand how American culture is changing? So does a budding sociologist in Indiana. He’s using data science to find patterns in the massive amounts of text people use each day to express their worldviews — patterns that no individual reader would be able to recognize.

Intelligent people find new uses for data science every day. Still, despite the explosion of interest in the data collected by just about every sector of American business — from financial companies and health care firms to management consultancies and the government — many organizations continue to relegate data-science knowledge to a small number of employees.

That’s a mistake — and in the long run, it’s unsustainable. Think of it this way: Very few companies expect only professional writers to know how to write. So why ask onlyprofessional data scientists to understand and analyze data, at least at a basic level?

Relegating all data knowledge to a handful of people within a company is problematic on many levels. Data scientists find it frustrating because it’s hard for them to communicate their findings to colleagues who lack basic data literacy. Business stakeholders are unhappy because data requests take too long to fulfill and often fail to answer the original questions. In some cases, that’s because the questioner failed to explain the question properly to the data scientist.

Why would non–data scientists need to learn data science? That’s like asking why non-accountants should be expected to stay within budget.

These days every industry is drenched in data, and the organizations that succeed are those that most quickly make sense of their data in order to adapt to what’s coming. The best way to enable fast discovery and deeper insights is to disperse data science expertise across an organization.

Companies that want to compete in the age of data need to do three things: share data tools, spread data skills, and spread data responsibility…(More)”.

Data Colonialism: Rethinking Big Data’s Relation to the Contemporary Subject

Curated on Posted on by Stefaan Verhulst

Nick Couldry and Ulises Mejias in Television & New Media (TVNM): “...Data colonialism combines the predatory extractive practices of historical colonialism with the abstract quantification methods of computing. Understanding Big Data from the Global South means understanding capitalism’s current dependence on this new type of appropriation that works at every point in space where people or things are attached to today’s infrastructures of connection. The scale of this transformation means that it is premature to map the forms of capitalism that will emerge from it on a global scale. Just as historical colonialism over the long-run provided the essential preconditions for the emergence of industrial capitalism, so over time, we can expect that data colonialism will provide the preconditions for a new stage of capitalism that as yet we can barely imagine, but for which the appropriation of human life through data will be central.

Right now, the priority is not to speculate about that eventual stage of capitalism, but to resist the data colonialism that is under way. This is how we understand Big Data from the South. Through what we call ‘data relations’ (new types of human relations which enable the extraction of data for commodification), social life all over the globe becomes an ‘open’ resource for extraction that is somehow ‘just there’ for capital. These global flows of data are as expansive as historic colonialism’s appropriation of land, resources, and bodies, although the epicentre has somewhat shifted. Data colonialism involves not one pole of colonial power (‘the West’), but at least two: the USA and China. This complicates our notion of the geography of the Global South, a concept which until now helped situate resistance and disidentification along geographic divisions between former colonizers and colonized. Instead, the new data colonialism works both externally — on a global scale — and internally on its own home populations. The elites of data colonialism (think of Facebook) benefit from colonization in both dimensions, and North-South, East-West divisions no longer matter in the same way.

It is important to acknowledge both the apparent similarities and the significant differences between our argument and the many preceding critical arguments about Big Data…(More)”