Selected Readings on Data Responsibility, Refugees and Migration

Curated on Posted on by Andrew Young

By Kezia Paladina, Alexandra Shaw, Michelle Winowatan, Stefaan Verhulst, and Andrew Young

The Living Library’s Selected Readings series seeks to build a knowledge base on innovative approaches for improving the effectiveness and legitimacy of governance. This curated and annotated collection of recommended works on the topic of Data Collaboration for Migration was originally published in 2018.

Special thanks to Paul Currion whose data responsibility literature review gave us a headstart when developing the below. (Check out his article listed below on Refugee Identity)

The collection below is also meant to complement our article in the Stanford Social Innovation Review on Data Collaboration for Migration where we emphasize the need for a Data Responsibility Framework moving forward.

From climate change to politics to finance, there is growing recognition that some of the most intractable problems of our era are information problems. In recent years, the ongoing refugee crisis has increased the call for new data-driven approaches to address the many challenges and opportunities arising from migration. While data – including data from the private sector – holds significant potential value for informing analysis and targeted international and humanitarian response to (forced) migration, decision-makers often lack an actionable understanding of if, when and how data could be collected, processed, stored, analyzed, used, and shared in a responsible manner.

Data responsibility – including the responsibility to protect data and shield its subjects from harms, and the responsibility to leverage and share data when it can provide public value – is an emerging field seeking to go beyond just privacy concerns. The forced migration arena has a number of particularly important issues impacting responsible data approaches, including the risks of leveraging data regarding individuals fleeing a hostile or repressive government.

In this edition of the GovLab’s Selected Readings series, we examine the emerging literature on the data responsibility approaches in the refugee and forced migration space – part of an ongoing series focused on Data Responsibiltiy. The below reading list features annotated readings related to the Policy and Practice of data responsibility for refugees, and the specific responsibility challenges regarding Identity and Biometrics.

Data Responsibility and Refugees – Policy and Practice

International Organization for Migration (IOM) (2010) IOM Data Protection Manual. Geneva: IOM.

  • This IOM manual includes 13 data protection principles related to the following activities: lawful and fair collection, specified and legitimate purpose, data quality, consent, transfer to third parties, confidentiality, access and transparency, data security, retention and personal data, application of the principles, ownership of personal data, oversight, compliance and internal remedies (and exceptions).
  • For each principle, the IOM manual features targeted data protection guidelines, and templates and checklists are included to help foster practical application.

Norwegian Refugee Council (NRC) Internal Displacement Monitoring Centre / OCHA (eds.) (2008) Guidance on Profiling Internally Displaced Persons. Geneva: Inter-Agency Standing Committee.

  • This NRC document contains guidelines on gathering better data on Internally Displaced Persons (IDPs), based on country context.
  • IDP profile is defined as number of displaced persons, location, causes of displacement, patterns of displacement, and humanitarian needs among others.
  • It further states that collecting IDPs data is challenging and the current condition of IDPs data are hampering assistance programs.
  • Chapter I of the document explores the rationale for IDP profiling. Chapter II describes the who aspect of profiling: who IDPs are and common pitfalls in distinguishing them from other population groups. Chapter III describes the different methodologies that can be used in different contexts and suggesting some of the advantages and disadvantages of each, what kind of information is needed and when it is appropriate to profile.

United Nations High Commissioner for Refugees (UNHCR). Model agreement on the sharing of personal data with Governments in the context of hand-over of the refugee status determination process. Geneva: UNHCR.

  • This document from UNHCR provides a template of agreement guiding the sharing of data between a national government and UNHCR. The model agreement’s guidance is aimed at protecting the privacy and confidentiality of individual data while promoting improvements to service delivery for refugees.

United Nations High Commissioner for Refugees (UNHCR) (2015). Policy on the Protection of Personal Data of Persons of Concern to UNHCR. Geneva: UNHCR.

  • This policy outlines the rules and principles regarding the processing of personal data of persons engaged by UNHCR with the purpose of ensuring that the practice is consistent with UNGA’s regulation of computerized personal data files that was established to protect individuals’ data and privacy.
  • UNHCR require its personnel to apply the following principles when processing personal data: (i) Legitimate and fair processing (ii) Purpose specification (iii) Necessity and proportionality (iv) Accuracy (v) Respect for the rights of the data subject (vi) Confidentiality (vii) Security (viii) Accountability and supervision.

United Nations High Commissioner for Refugees (UNHCR) (2015) Privacy Impact Assessment of UNHCR Cash Based Interventions.

  • This impact assessment focuses on privacy issues related to financial assistance for refugees in the form of cash transfers. For international organizations like UNHCR to determine eligibility for cash assistance, data “aggregation, profiling, and social sorting techniques,” are often needed, leading a need for a responsible data approach.
  • This Privacy Impact Assessment (PIA) aims to identify the privacy risks posed by their program and seek to enhance safeguards that can mitigate those risks.
  • Key issues raised in the PIA involves the challenge of ensuring that individuals’ data will not be used for purposes other than those initially specified.

Data Responsibility in Identity and Biometrics

Bohlin, A. (2008) “Protection at the Cost of Privacy? A Study of the Biometric Registration of Refugees.” Lund: Faculty of Law of the University of Lund.

  • This 2008 study focuses on the systematic biometric registration of refugees conducted by UNHCR in refugee camps around the world, to understand whether enhancing the registration mechanism of refugees contributes to their protection and guarantee of human rights, or whether refugee registration exposes people to invasions of privacy.
  • Bohlin found that, at the time, UNHCR failed to put a proper safeguards in the case of data dissemination, exposing the refugees data to the risk of being misused. She goes on to suggest data protection regulations that could be put in place in order to protect refugees’ privacy.

Currion, Paul. (2018) “The Refugee Identity.” Medium.

  • Developed as part of a DFID-funded initiative, this essay considers Data Requirements for Service Delivery within Refugee Camps, with a particular focus on refugee identity.
  • Among other findings, Currion finds that since “the digitisation of aid has already begun…aid agencies must therefore pay more attention to the way in which identity systems affect the lives and livelihoods of the forcibly displaced, both positively and negatively.”
  • Currion argues that a Responsible Data approach, as opposed to a process defined by a Data Minimization principle, provides “useful guidelines,” but notes that data responsibility “still needs to be translated into organisational policy, then into institutional processes, and finally into operational practice.”

Farraj, A. (2010) “Refugees and the Biometric Future: The Impact of Biometrics on Refugees and Asylum Seekers.” Colum. Hum. Rts. L. Rev. 42 (2010): 891.

  • This article argues that biometrics help refugees and asylum seekers establish their identity, which is important for ensuring the protection of their rights and service delivery.
  • However, Farraj also describes several risks related to biometrics, such as, misidentification and misuse of data, leading to a need for proper approaches for the collection, storage, and utilization of the biometric information by government, international organizations, or other parties.  

GSMA (2017) Landscape Report: Mobile Money, Humanitarian Cash Transfers and Displaced Populations. London: GSMA.

  • This paper from GSMA seeks to evaluate how mobile technology can be helpful in refugee registration, cross-organizational data sharing, and service delivery processes.
  • One of its assessments is that the use of mobile money in a humanitarian context depends on the supporting regulatory environment that contributes to unlocking the true potential of mobile money. The examples include extension of SIM dormancy period to anticipate infrequent cash disbursements, ensuring that persons without identification are able to use the mobile money services, and so on.
  • Additionally, GMSA argues that mobile money will be most successful when there is an ecosystem to support other financial services such as remittances, airtime top-ups, savings, and bill payments. These services will be especially helpful in including displaced populations in development.

GSMA (2017) Refugees and Identity: Considerations for mobile-enabled registration and aid delivery. London: GSMA.

  • This paper emphasizes the importance of registration in the context of humanitarian emergency, because being registered and having a document that proves this registration is key in acquiring services and assistance.
  • Studying cases of Kenya and Iraq, the report concludes by providing three recommendations to improve mobile data collection and registration processes: 1) establish more flexible KYC for mobile money because where refugees are not able to meet existing requirements; 2) encourage interoperability and data sharing to avoid fragmented and duplicative registration management; and 3) build partnership and collaboration among governments, humanitarian organizations, and multinational corporations.

Jacobsen, Katja Lindskov (2015) “Experimentation in Humanitarian Locations: UNHCR and Biometric Registration of Afghan Refugees.” Security Dialogue, Vol 46 No. 2: 144–164.

  • In this article, Jacobsen studies the biometric registration of Afghan refugees, and considers how “humanitarian refugee biometrics produces digital refugees at risk of exposure to new forms of intrusion and insecurity.”

Jacobsen, Katja Lindskov (2017) “On Humanitarian Refugee Biometrics and New Forms of Intervention.” Journal of Intervention and Statebuilding, 1–23.

  • This article traces the evolution of the use of biometrics at the Office of the United Nations High Commissioner for Refugees (UNHCR) – moving from a few early pilot projects (in the early-to-mid-2000s) to the emergence of a policy in which biometric registration is considered a ‘strategic decision’.

Manby, Bronwen (2016) “Identification in the Context of Forced Displacement.” Washington DC: World Bank Group. Accessed August 21, 2017.

  • In this paper, Bronwen describes the consequences of not having an identity in a situation of forced displacement. It prevents displaced population from getting various services and creates higher chance of exploitation. It also lowers the effectiveness of humanitarian actions, as lacking identity prevents humanitarian organizations from delivering their services to the displaced populations.
  • Lack of identity can be both the consequence and and cause of forced displacement. People who have no identity can be considered illegal and risk being deported. At the same time, conflicts that lead to displacement can also result in loss of ID during travel.
  • The paper identifies different stakeholders and their interest in the case of identity and forced displacement, and finds that the biggest challenge for providing identity to refugees is the politics of identification and nationality.
  • Manby concludes that in order to address this challenge, there needs to be more effective coordination among governments, international organizations, and the private sector to come up with an alternative of providing identification and services to the displaced persons. She also argues that it is essential to ensure that national identification becomes a universal practice for states.

McClure, D. and Menchi, B. (2015). Challenges and the State of Play of Interoperability in Cash Transfer Programming. Geneva: UNHCR/World Vision International.

  • This report reviews the elements that contribute to the interoperability design for Cash Transfer Programming (CTP). The design framework offered here maps out these various features and also looks at the state of the problem and the state of play through a variety of use cases.
  • The study considers the current state of play and provides insights about the ways to address the multi-dimensionality of interoperability measures in increasingly complex ecosystems.     

NRC / International Human Rights Clinic (2016). Securing Status: Syrian refugees and the documentation of legal status, identity, and family relationships in Jordan.

  • This report examines Syrian refugees’ attempts to obtain identity cards and other forms of legally recognized documentation (mainly, Ministry of Interior Service Cards, or “new MoI cards”) in Jordan through the state’s Urban Verification Exercise (“UVE”). These MoI cards are significant because they allow Syrians to live outside of refugee camps and move freely about Jordan.
  • The text reviews the acquirement processes and the subsequent challenges and consequences that refugees face when unable to obtain documentation. Refugees can encounter issues ranging from lack of access to basic services to arrest, detention, forced relocation to camps and refoulement.  
  • Seventy-two Syrian refugee families in Jordan were interviewed in 2016 for this report and their experiences with obtaining MoI cards varied widely.

Office of Internal Oversight Services (2015). Audit of the operations in Jordan for the Office of the United Nations High Commissioner for Refugees. Report 2015/049. New York: UN.

  • This report documents the January 1, 2012 – March 31, 2014 audit of Jordanian operations, which is intended to ensure the effectiveness of the UNHCR Representation in the state.
  • The main goals of the Regional Response Plan for Syrian refugees included relieving the pressure on Jordanian services and resources while still maintaining protection for refugees.
  • The audit results concluded that the Representation was initially unsatisfactory, and the OIOS suggested several recommendations according to the two key controls which the Representation acknowledged. Those recommendations included:
    • Project management:
      • Providing training to staff involved in financial verification of partners supervise management
      • Revising standard operating procedure on cash based interventions
      • Establishing ways to ensure that appropriate criteria for payment of all types of costs to partners’ staff are included in partnership agreements
    • Regulatory framework:
      • Preparing annual need-based procurement plan and establishing adequate management oversight processes
      • Creating procedures for the assessment of renovation work in progress and issuing written change orders
      • Protecting data and ensuring timely consultation with the UNHCR Division of Financial and Administrative Management

UNHCR/WFP (2015). Joint Inspection of the Biometrics Identification System for Food Distribution in Kenya. Geneva: UNHCR/WFP.

  • This report outlines the partnership between the WFP and UNHCR in its effort to promote its biometric identification checking system to support food distribution in the Dadaab and Kakuma refugee camps in Kenya.
  • Both entities conducted a joint inspection mission in March 2015 and was considered an effective tool and a model for other country operations.
  • Still, 11 recommendations are proposed and responded to in this text to further improve the efficiency of the biometric system, including real-time evaluation of impact, need for automatic alerts, documentation of best practices, among others.

Replicating the Justice Data Lab in the USA: Key Considerations

Curated on Posted on by Stefaan Verhulst

Blog by Tracey Gyateng and Tris Lumley: “Since 2011, NPC has researched, supported and advocated for the development of impact-focussed Data Labs in the UK. The goal has been to unlock government administrative data so that organisations (primarily nonprofits) who provide a social service can understand the impact of their services on the people who use them.

So far, one of these Data Labs has been developed to measure re-offending outcomes- the Justice Data Lab-, and others are currently being piloted for employment and education. Given our seven years of work in this area, we at NPC have decided to reflect on the key factors needed to create a Data Lab with our report: How to Create an Impact Data Lab. This blog outlines these factors, examines whether they are present in the USA, and asks what the next steps should be — drawing on the research undertaken with the Governance Lab….Below we examine the key factors and to what extent they appear to be present within the USA.

Environment: A broad culture that supports impact measurement. Similar to the UK, nonprofits in the USA are increasingly measuring the impact they have had on the participants of their service and sharing the difficulties of undertaking robust, high quality evaluations.

Data: Individual person-level administrative data. A key difference between the two countries is that, in the USA, personal data on social services tends to be held at a local, rather than central level. In the UK social services data such as reoffending, education and employment are collated into a central database. In the USA, the federal government has limited centrally collated personal data, instead this data can be found at state/city level….

A leading advocate: A Data Lab project team, and strong networks. Data Labs do not manifest by themselves. They requires a lead agency to campaign with, and on behalf of, nonprofits to set out a persuasive case for their development. In the USA, we have developed a partnership with the Governance Lab to seek out opportunities where Data Labs can be established but given the size of the country, there is scope for further collaborations/ and or advocates to be identified and supported.

Customers: Identifiable organisations that would use the Data Lab. Initial discussions with several US nonprofits and academia indicate support for a Data Lab in their context. Broad consultation based on an agreed region and outcome(s) will be needed to fully assess the potential customer base.

Data owners: Engaged civil servants. Generating buy-in and persuading various stakeholders including data owners, analysts and politicians is a critical part of setting up a data lab. While the exact profiles of the right people to approach can only be assessed once a region and outcome(s) of interest have been chosen, there are encouraging signs, such as the passing of the Foundations for Evidence-Based Policy Making Act of 2017 in the house of representatives which, among other things, mandates the appointment of “Chief Evaluation Officers” in government departments- suggesting that there is bipartisan support for increased data-driven policy evaluation.

Legal and ethical governance: A legal framework for sharing data. In the UK, all personal data is subject to data protection legislation, which provides standardised governance for how personal data can be processed across the country and within the European Union. A universal data protection framework does not exist within the USA, therefore data sharing agreements between customers and government data-owners will need to be designed for the purposes of Data Labs, unless there are existing agreements that enable data sharing for research purposes. This will need to be investigated at the state/city level of a desired Data Lab.

Funding: Resource and support for driving the set-up of the Data Lab. Most of our policy lab case studies were funded by a mixture of philanthropy and government grants. It is expected that a similar mixed funding model will need to be created to establish Data Labs. One alternative is the model adopted by the Washington State Institute for Public Policy (WSIPP), which was created by the Washington State Legislature and is funded on a project basis, primarily by the state. Additionally funding will be needed to enable advocates of a Data Lab to campaign for the service….(More)”.

How Democracy Can Survive Big Data

Curated on Posted on by Stefaan Verhulst

Colin Koopman in The New York Times: “…The challenge of designing ethics into data technologies is formidable. This is in part because it requires overcoming a century-long ethos of data science: Develop first, question later. Datafication first, regulation afterward. A glimpse at the history of data science shows as much.

The techniques that Cambridge Analytica uses to produce its psychometric profiles are the cutting edge of data-driven methodologies first devised 100 years ago. The science of personality research was born in 1917. That year, in the midst of America’s fevered entry into war, Robert Sessions Woodworth of Columbia University created the Personal Data Sheet, a questionnaire that promised to assess the personalities of Army recruits. The war ended before Woodworth’s psychological instrument was ready for deployment, but the Army had envisioned its use according to the precedent set by the intelligence tests it had been administering to new recruits under the direction of Robert Yerkes, a professor of psychology at Harvard at the time. The data these tests could produce would help decide who should go to the fronts, who was fit to lead and who should stay well behind the lines.

The stakes of those wartime decisions were particularly stark, but the aftermath of those psychometric instruments is even more unsettling. As the century progressed, such tests — I.Q. tests, college placement exams, predictive behavioral assessments — would affect the lives of millions of Americans. Schoolchildren who may have once or twice acted out in such a way as to prompt a psychometric evaluation could find themselves labeled, setting them on an inescapable track through the education system.

Researchers like Woodworth and Yerkes (or their Stanford colleague Lewis Terman, who formalized the first SAT) did not anticipate the deep consequences of their work; they were too busy pursuing the great intellectual challenges of their day, much like Mr. Zuckerberg in his pursuit of the next great social media platform. Or like Cambridge Analytica’s Christopher Wylie, the twentysomething data scientist who helped build psychometric profiles of two-thirds of all Americans by leveraging personal information gained through uninformed consent. All of these researchers were, quite understandably, obsessed with the great data science challenges of their generation. Their failure to consider the consequences of their pursuits, however, is not so much their fault as it is our collective failing.

For the past 100 years we have been chasing visions of data with a singular passion. Many of the best minds of each new generation have devoted themselves to delivering on the inspired data science promises of their day: intelligence testing, building the computer, cracking the genetic code, creating the internet, and now this. We have in the course of a single century built an entire society, economy and culture that runs on information. Yet we have hardly begun to engineer data ethics appropriate for our extraordinary information carnival. If we do not do so soon, data will drive democracy, and we may well lose our chance to do anything about it….(More)”.

Empowerment tool for women maps cases of harassment

Curated on Posted on by Stefaan Verhulst

Springwise: “We have previously written about innovations that promote inclusion and equal rights such as edible pie charts that highlight gender inequality. Another example is a predictive text app that finds alternative words for gendered language. Now, NINA, created in Brazil, is an app for empowering women to report violence that occurs in public spaces. The project was shared to Red Bull Amaphiko, a platform for social entrepreneurs to share their work and stories.

A 2016 survey released by ActionAid and conducted by YouGov found that 86 percent of Brazilian women were victims of harassment in public spaces. Responding to these statistics, Simony César created project NINA two years ago to help tackle gender-based violence. The app collects data in real time, mapping locations in which cases of harassment have taken place. The launch and testing of the app took place on public transport. It saw 76 thousand users per day at 17 bus lines at the Federal University of Pernambuco (UFPE).

César states “The premise of NINA aims to empower women through an application that denounces the types of violence they suffer within public spaces”. It combats violence against women by making cases of harassment in the city locatable on a map. NINA can then use this data to find out which bus lines have the highest rate of harassment. It can also record the most common times that cases occur and store photographic records and short videos of harassers.

Another survey by ActionAid in March 2018 revealed that 64 percent of Brazilian women surveyed were victims of sexual harassment. These results demonstrate that the need for empowerment tools, such as NINA, is still necessary. The exposure of women to violence in public city spaces is a global issue and as a result, accessibility within cities is unequal based on gender….(More)”.

Algorithmic Injustice

Curated on Posted on by Stefaan Verhulst

Tafari Mbadiwe at The Atlantis: “For generations, the Maasai people of eastern Africa have passed down the story of a tireless old man. He lived alone and his life was not easy. He spent every day in the fields — tilling the land, tending the animals, and gathering water. The work was as necessary as it was exhausting. But the old man considered himself fortunate. He had a good life, and never really gave much thought to what was missing.

One morning the old man was greeted with a pleasant surprise. Standing in his kitchen was a young boy, perhaps seven or eight years old. The old man had never seen him before. The boy smiled but said nothing. The old man looked around. His morning breakfast had already been prepared, just as he liked it. He asked the boy’s name. “Kileken,” the boy replied. After some prodding, the boy explained that, before preparing breakfast, he had completed all of the old man’s work for the day. Incredulous, the old man stepped outside. Indeed, the fields had been tilled, the animals tended, and the water gathered. Astonishment written all over his face, the old man staggered back into the kitchen. “How did this happen? And how can I repay you?” The boy smiled again, this time dismissively. “I will accept no payment. All I ask is that you let me stay with you.” The old man knew better than to look a gift horse in the mouth.

Kileken and the old man soon became inseparable, and the farm grew lush and bountiful as it never had before. The old man could hardly remember what life was like before the arrival of his young comrade. There could be no doubt: With Kileken’s mysterious assistance, the old man was prospering. But he never quite understood why, or how, it had happened.

To an extent we have failed to fully acknowledge, decision-making algorithms have become our society’s collective Kileken. They show up unannounced and where we least expect them, promise and often deliver great things, and quickly come to be seen as indispensable. Their reach can’t be overestimated. They tell traders what stocks to buy and sell, determine how much our car insurance costs, influence which products Amazon shows us and how much we get charged for them, and interpret our Google searches and rank their results….(More)”.

Can Data Help Brazil Take a Bite Out of Crime?

Curated on Posted on by Stefaan Verhulst

Joe Leahy at ZY See Beyond: “When Argentine entrepreneur Federico Vega two years ago launched a startup offering Uberlike services for Brazil’s freight industry, the sector was on the cusp of a wave of cargo theft.

Across Brazil, but especially in Rio de Janeiro, crime has soared, with armed gangs robbing one truck every 50 minutes in Rio last year.

But while the authorities have reacted with force to the crime wave, Vega turned to software engineers at his CargoX startup. By studying a range of industry and security data, CargoX developed software that identifies risks and helps drivers avoid crime hot spots, or if a robbery does happen, alerts the company in real time.CargoX says that in Brazil, 0.1 percent by value of all cargo transported by trucks is stolen. “We are about 50 percent lower than that, but we still have tons of work to do,” says São Paulo–based Vega.

CargoX is one of a growing number of Brazilian technology startups that are seeking digital solutions to the problem of endemic crime in Latin America’s largest country.

Having started from zero two years ago, CargoX today has signed up more than 5,000 truckers. The company scans data from all sources to screen its motorists and study past crimes to see what routes, times, neighborhoods and types of cargo represent the highest risk.

Certain gas stations that might, for instance, be known for prostitution are avoided because of their criminal associations. Daytime delivery is better than night. Drivers are tracked by GPS and must stay inside “geofences” — known safe routes. Foraying outside these alerts the system.

Vega says the key is to learn from the data. “Everyone says it’s good to learn from your mistakes, but it’s even better to learn from other people’s mistakes.”

The use of big data to anticipate crime is at the center of the approach of another tech-savvy entrepreneur, Pedro Moura Costa, the founder of BVRio Institute, an organization that seeks market solutions to environmental issues.

Organized crime is targeting everything from highway robbery to the illegal plunder of tropical hardwoods in the Amazon while online crime such as credit card fraud is also rampant, analysts say….(More)”.

How the government will operate in 2030

Curated on Posted on by Stefaan Verhulst

Darrell West at the Hill: “Imagine it is 2030 and you are a U.S. government employee working from home. With the assistance of the latest technology, you participate in video calls with clients and colleagues, augment your job activities through artificial intelligence and a personal digital assistant, work through collaboration software, and regularly get rated on a one-to-five scale by clients regarding your helpfulness, follow-through, and task completion.

How did you — and the government — get here? The sharing economy that unfolded in 2018 has revolutionized the public-sector workforce. The days when federal employees were subject to a centrally directed Office of Personnel and Management that oversaw permanent, full-time workers sitting in downtown office buildings are long gone. In their place is a remote workforce staffed by a mix of short- and long-term employees. This has dramatically improved worker productivity and satisfaction.

In the new digital world that has emerged, the goal is to use technology to make employees accountable. Gone are 20- or 30-year careers in the federal bureaucracy. Political leaders have always preached the virtue of running government like a business, and the success of Uber, Airbnb, and WeWork has persuaded them to focus on accountability and performance.

Companies such as Facebook demonstrated they could run large and complex organizations with less than 20,000 employees, and the federal government followed suit in the late 2020s. Now, workers deploy the latest tools of artificial intelligence, virtual reality, data analytics, robots, driverless cars, and digital assistants to improve the government. Unlike the widespread mistrust and cynicism that had poisoned attitudes in the decades before, the general public now sees government as a force for achieving positive results.

Many parts of the federal government are decentralized and mid-level employees are given greater authority to make decisions — but are subject to digital ratings that keep them accountable for their performance. The U.S. government borrowed this technique from China, where airport authorities in 2018 installed digital devices that allowed visitors to rate the performance of individual passport officers after every encounter. The reams of data have enabled Chinese authorities to fire poor performers and make sure foreign visitors see a friendly and competent face at the Beijing International Airport.

Alexa-like devices are given to all federal employees. The devices are used to keep track of leave time, file reimbursement requests, request time off, and complete a range of routine tasks that used to take employees hours. Through voice-activated commands, they navigate these mundane tasks quickly and efficiently. No one can believe the mountains of paperwork required just a decade ago….(More)”.

Data for Development: What’s next? Concepts, trends and recommendations

Curated on Posted on by Stefaan Verhulst

Report by the WebFoundation: “The exponential growth of data provides powerful new ways for governments and companies to understand and respond to challenges and opportunities. This report, Data for Development: What’s next, investigates how organisations working in international development can leverage the growing quantity and variety of data to improve their investments and projects so that they better meet people’s needs.

Investigating the state of data for development and identifying emerging data trends, the study provides recommendations to support German development cooperation actors seeking to integrate data strategies and investments in their work. These insights can guide any organisation seeking to use data to enhance their development work.

The research considers four types of data: (1) big data, (2) open data, (3) citizen-generated data and (4) real-time data, and examines how they are currently being used in development-related policy-making and how they might lead to better development outcomes….(More)”.

Cambridge Analytica scandal: legitimate researchers using Facebook data could be collateral damage

Curated on Posted on by Stefaan Verhulst

 at The Conversation: “The scandal that has erupted around Cambridge Analytica’s alleged harvesting of 50m Facebook profiles assembled from data provided by a UK-based academic and his company is a worrying development for legitimate researchers.

Political data analytics company Cambridge Analytica – which is affiliated with Strategic Communication Laboratories (SCL) – reportedly used Facebook data, after it was handed over by Aleksandr Kogan, a lecturer at the University of Cambridge’s department of psychology.

Kogan, through his company Global Science Research (GSR) – separate from his university work – gleaned the data from a personality test app named “thisisyourdigitallife”. Roughly 270,000 US-based Facebook users voluntarily responded to the test in 2014. But the app also collected data on those participants’ Facebook friends without their consent.

This was possible due to Facebook rules at the time that allowed third-party apps to collect data about a Facebook user’s friends. The Mark Zuckerberg-run company has since changed its policy to prevent such access to developers….

Social media data is a rich source of information for many areas of research in psychology, technology, business and humanities. Some recent examples include using Facebook to predict riots, comparing the use of Facebook with body image concern in adolescent girls and investigating whether Facebook can lower levels of stress responses, with research suggesting that it may enhance and undermine psycho-social constructs related to well-being.

It is right to believe that researchers and their employers value research integrity. But instances where trust has been betrayed by an academic – even if it’s the case that data used for university research purposes wasn’t caught in the crossfire – will have a negative impact on whether participants will continue to trust researchers. It also has implications for research governance and for companies to share data with researchers in the first place.

Universities, research organisations and funders govern the integrity of research with clear and strict ethics proceduresdesigned to protect participants in studies, such as where social media data is used. The harvesting of data without permission from users is considered an unethical activity under commonly understood research standards.

The fallout from the Cambridge Analytica controversy is potentially huge for researchers who rely on social networks for their studies, where data is routinely shared with them for research purposes. Tech companies could become more reluctant to share data with researchers. Facebook is already extremely protective of its data – the worry is that it could become doubly difficult for researchers to legitimately access this information in light of what has happened with Cambridge Analytica….(More)”.

Coastal research increasingly depends on citizen scientists

Curated on Posted on by Stefaan Verhulst

Brenna Visser at CS Monitor: “…This monthly ritual is a part of the COASST survey, a program that relies on data taken by volunteers to study large-scale patterns in seabird populations on the West Coast. The Haystack Rock Awareness Program conducts similar surveys for sea stars and marine debris throughout the year.

Surveys like these play a small part in a growing trend in the science community to use citizen scientists as a way to gather massive amounts of data. Over the weekend, marine scientists and conservationists came to Cannon Beach for an annual Coast Conference, a region wide event to discuss coastal science and stewardship.

Whether the presentation was about ocean debris, marine mammals, seabirds, or ocean jellies, many relied on the data collection work of volunteers throughout the state. A database for citizen science programs called Citsci.org, which recorded only a few dozen groups 10 years ago, now has more than 500 groups registered across the country, with new ones registering every day….

Part of the rise has to do with technology, she said. Apps that help identify species and allow unprecedented access to information have driven interest up and removed barriers that would have otherwise made it harder to collect data without formal training. Another is the science community slowly coming around to accept citizen science.

“I think there’s a lot of reticence in the science community to use citizen science. There’s some doubt the data collected is of the precision or accuracy that is needed to document phenomena,” Parrish said. “But as it grows, the more standardized it becomes. What we’re seeing right now is a lot of discussion in citizen science programs asking what they need to do to get to that level.”…While a general decline in federal funding for scientific research could play a factor in the science community’s acceptance of using volunteer-collected data, Parrish said, regardless of funding, there are some projects only citizen scientists can accomplish….(More)”