App facilitates charity work in Jordan

Curated on Posted on by Stefaan Verhulst

Springwise: “We have already seen how technology can be harnessed to help facilitate charitable and environmental efforts. For example, the recycling organization which helps businesses rehome unwanted goods, donating money to charity in addition to helping businesses be more economical. Another example in which technology has been used to raise awareness is through the charity chatbot, which teaches users about women’s daily journey to find water in Ethiopia.

JoodLife is a start-up which aims to make the most of technology and take advantage of it in order to help voluntary efforts in Jordan.

The application works as a social platform to connect volunteers and donors in order to facilitate charity work. Donors can register their donations via the app, and then all the available grants are displayed. The grants can be searched for on the app, and users can specify the area they wish to search. The donor and the volunteer can then agree a mechanism by which they wish to transfer the grant. At which point the available grant will no longer be shown on the app search. The app aims to serve as a link between donors and volunteers to save both parties time and effort. This makes it much easier to make monetary and material donations. The social aspect of the app also increases solidarity between charity workers and makes it much simpler to distribute roles in the most efficient way….(More)”.

Making sense of evidence: A guide to using evidence in policy

Curated on Posted on by Stefaan Verhulst

Handbook by the Government of New Zealand: “…helps you take a structured approach to using evidence at every stage of the policy and programme development cycle. Whether you work for central or local government, or the community and voluntary sector, you’ll find advice to help you:

  • understand different types and sources of evidence
  • know what you can learn from evidence
  • appraise evidence and rate its quality
  • decide how to select and use evidence to the best effect
  • take into account different cultural values and knowledge systems
  • be transparent about how you’ve considered evidence in your policy development work…(More)”

(See also Summary; This handbook is a companion to Making sense of evaluation: A handbook for everyone.).

Algorithmic Impact Assessment (AIA) framework

Curated on Posted on by Stefaan Verhulst

Report by AINow Institute: “Automated decision systems are currently being used by public agencies, reshaping how criminal justice systems work via risk assessment algorithms1 and predictive policing, optimizing energy use in critical infrastructure through AI-driven resource allocation, and changing our employment4 and educational systems through automated evaluation tools and matching algorithms.Researchers, advocates, and policymakers are debating when and where automated decision systems are appropriate, including whether they are appropriate at all in particularly sensitive domains.

Questions are being raised about how to fully assess the short and long term impacts of these systems, whose interests they serve, and if they are sufficiently sophisticated to contend with complex social and historical contexts. These questions are essential, and developing strong answers has been hampered in part by a lack of information and access to the systems under deliberation. Many such systems operate as “black boxes” – opaque software tools working outside the scope of meaningful scrutiny and accountability.8 This is concerning, since an informed policy debate is impossible without the ability to understand which existing systems are being used, how they are employed, and whether these systems cause unintended consequences. The Algorithmic Impact Assessment (AIA) framework proposed in this report is designed to support affected communities and stakeholders as they seek to assess the claims made about these systems, and to determine where – or if – their use is acceptable….

KEY ELEMENTS OF A PUBLIC AGENCY ALGORITHMIC IMPACT ASSESSMENT

1. Agencies should conduct a self-assessment of existing and proposed automated decision systems, evaluating potential impacts on fairness, justice, bias, or other concerns across affected communities;

2. Agencies should develop meaningful external researcher review processes to discover, measure, or track impacts over time;

3. Agencies should provide notice to the public disclosing their definition of “automated decision system,” existing and proposed systems, and any related self-assessments and researcher review processes before the system has been acquired;

4. Agencies should solicit public comments to clarify concerns and answer outstanding questions; and

5. Governments should provide enhanced due process mechanisms for affected individuals or communities to challenge inadequate assessments or unfair, biased, or otherwise harmful system uses that agencies have failed to mitigate or correct….(More)”.

The Power Of The Wikimedia Movement Beyond Wikimedia

Curated on Posted on by Stefaan Verhulst

Michael Bernick at Forbes: “In January 2017, we the constituents of Wikimedia, started an ambitious discussion about our collective future. We reflected on our past sixteen years together and imagined the impact we could have in the world in the next decades. Our aim was to identify a common strategic direction that would unite and inspire people across our movement on our way to 2030, and help us make decisions.”…

The final documents included a strategic direction and a research report: “Wikimedia 2030: Wikimedia’s Role in Shaping the Future of the Information Commons”: an expansive look at Wikimedia, knowledge, technologies, and communications in the next decade. It includes thoughtful sections on Demographics (global population trends, and Wikimedia’s opportunities for growth) Emerging Platforms (how Wikimedia platforms will be accessed), Misinformation (how content creators and technologists can work toward a product that is trustworthy), Literacy (changing forms of learning that can benefit from the Wikimedia movement) and the core Wikimedia issues of Open Knowledge and knowledge as a service.

Among its goals, the document calls for greater outreach to areas outside of Europe and North America (which now account for 63% of Wikimedia’s total traffic), and widening the knowledge and experiential bases of contributors. It urges greater access through mobile devices and other emerging hardware; and expanding partnerships with libraries, museums, galleries and archives.

The document captures not only the idealism of the enterprise, and but also why Wikimedia can be described as a movement not only an enterprise. It calls into question conventional wisdoms of how our political and business structures should operate.

Consider the Wikimedia editing process that seeks to reach common ground on contentious issues. Lisa Gruwell, the Chief Advancement Officer of the Wikimedia Foundation, notes that in the development of an article, often editors with diverging claims and views will weigh in.  Rather than escalating divisions, the process of editing has been found to reduce these divisions. Gruwell explains,

Through the collaborative editing process, the editors have critical discussions about what reliable sources say about a topic. They have to engage and defend their own perspectives about how an article should be represented, and ultimately find some form of common ground with other editors.

A number of researchers at Harvard Business School led by Shane Greenstein, Yuan Gu and Feng Zhu actually set out to study this phenomenon. Their findings, published in 2017 as a Harvard Business School working paper found that editors with different political viewpoints tended to dialogue with each other, and over time reduce rather than increase partisanship….(More)”.

The Potential and Practice of Data Collaboratives for Migration

Curated on Posted on by Stefaan Verhulst

Essay by Stefaan Verhulst and Andrew Young in the Stanford Social Innovation Review: “According to recent United Nations estimates, there are globally about 258 million international migrants, meaning people who live in a country other than the one in which they were born; this represents an increase of 49 percent since 2000. Of those, 26 million people have been forcibly displaced across borders, having migrated either as refugees or asylum seekers. An additional 40 million or so people are internally displaced due to conflict and violence, and millions more are displaced each year because of natural disasters. It is sobering, then, to consider that, according to many observers, global warming is likely to make the situation worse.

Migration flows of all kinds—for work, family reunification, or political or environmental reasons—create a range of both opportunities and challenges for nation states and international actors. But the issues associated with refugees and asylum seekers are particularly complex. Despite the high stakes and increased attention to the issue, our understanding of the full dimensions and root causes of refugee movements remains limited. Refugee flows arise in response to not only push factors like wars and economic insecurity, but also powerful pull factors in recipient countries, including economic opportunities, and perceived goods like greater tolerance and rule of law. In addition, more objectively measurable variables like border barriers, topography, and even the weather, play an important role in determining the number and pattern of refugee flows. These push and pull factors interact in complex and often unpredictable ways. Further complicating matters, some experts argue that push-pull research on migration is dogged by a number of conceptual and methodological limitations.

To mitigate negative impacts and anticipate opportunities arising from high levels of global migration, we need a better understanding of the various factors contributing to the international movement of people and how they work together.

Data—specifically, the widely dispersed data sets that exist across governments, the private sector, and civil society—can help alleviate today’s information shortcoming. Several recent initiatives show the potential of using data to address some of the underlying informational gaps. In particular, there is an important role for a new form of data-driven problem-solving and policymaking—what we call “data collaboratives.” Data collaboratives offer the potential for inter-sectoral collaboration, and for the merging and augmentation of otherwise siloed data sets. While public and private actors are increasingly experimenting with various types of data in a variety of sectors and geographies—including sharing disease data to accelerate disease treatments and leveraging private bus data to improve urban planning—we are only beginning to understand the potential of data collaboration in the context of migration and refugee issues….(More)”.

 

…(More)”

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)”.