10 transformative data questions related to gender


Press Release: “As part of efforts to identify priorities across sectors in which data and data science could make a difference, The Governance Lab (The GovLab) at the New York University Tandon School of Engineering has partnered with Data2X, the gender data alliance housed at the United Nations Foundation, to release ten pressing questions on gender that experts have determined can be answered using data. Members of the public are invited to share their views and vote to help develop a data agenda on gender.

The questions are part of the 100 Questions Initiative, an effort to identify the most important societal questions that can be answered by data. The project relies on an innovative process of sourcing “bilinguals,” individuals with both subject-matter and data expertise, who in this instance provided questions related to gender they considered to be urgent and answerable. The results span issues of labor, health, climate change, and gender-based violence.

Through the initiative’s new online platform, anyone can now vote on what they consider to be the most pressing, data-related questions about gender that researchers and institutions should prioritize. Through voting, the public can steer the conversation and determine which topics should be the subject of data collaboratives, an emerging form of collaboration that allows organizations from different sectors to exchange data to create public value.

The GovLab has conducted significant research on the value and practice of data collaboratives, and its research shows that inter-sectoral collaboration can both increase access to data as well as unleash the potential of that data to serve the public good.

Data2X supported the 100 Questions Initiative by providing expertise and connecting The GovLab with relevant communities, events, and resources. The initiative helped inform Data2X’s “Big Data, Big Impact? Towards Gender-Sensitive Data Systems” report, which identifies gaps of information on gender equality across key policy domains.

“Asking the right questions is a critical first step in fostering data production and encouraging data use to truly meet the unique experiences and needs of women and girls,” said Emily Courey Pryor, executive director of Data2X. “Obtaining public feedback is a crucial way to identify the most urgent questions — and to ultimately incentivize investment in gender data collection and use to find the answers.”Said Stefaan Verhulst, co-founder and chief research and development officer at The GovLab, “Sourcing and prioritizing questions related to gender can inform resource and funding allocation to address gender data gaps and support projects with the greatest potential impact. This way, we can be confident about solutions that address the challenges facing women and girls.”…(More)”.

Making Public Transit Fairer to Women Demands Way More Data


Flavie Halais at Wired: “Public transportation is sexist. This may be unintentional or implicit, but it’s also easy to see. Women around the world do more care and domestic work than men, and their resulting mobility habits are hobbled by most transport systems. The demands of running errands and caring for children and other family members mean repeatedly getting on and off the bus, meaning paying more fares. Strollers and shopping bags make travel cumbersome. A 2018 study of New Yorkers found women were harassed on the subway far more frequently than men were, and as a result paid more money to avoid transit in favor of taxis and ride-hail….

What is not measured is not known, and the world of transit data is still largely blind to women and other vulnerable populations. Getting that data, though, isn’t easy. Traditional sources like national censuses and user surveys provide reliable information that serve as the basis for policies and decisionmaking. But surveys are costly to run, and it can take years for a government to go through the process of adding a question to its national census.

Before pouring resources into costly data collection to find answers about women’s transport needs, cities could first turn to the trove of unconventional gender-disaggregated data that’s already produced. They include data exhaust, or the trail of data we leave behind as a result of our interactions with digital products and services like mobile phones, credit cards, and social media. Last year, researchers in Santiago, Chile, released a report based on their parsing of anonymized call detail records of female mobile phone users, to extract location information and analyze their mobility patterns. They found that women tended to travel to fewer locations than men, and within smaller geographical areas. When researchers cross-referenced location information with census data, they found a higher gender gap among lower-income residents, as poorer women made even shorter trips. And when using data from the local transit agency, they saw that living close to a public transit stop increased mobility for both men and women, but didn’t close the gender gap for poorer residents.

To encourage private companies to share such info, Stefaan Verhulst advocates for data collaboratives, flexible partnerships between data providers and researchers. Verhulst is the head of research and development at GovLab, a research center at New York University that contributed to the research in Santiago. And that’s how GovLab and its local research partner, Universidad del Desarollo, got access to the phone records owned by the Chilean phone company, Telefónica. Data collaboratives can enhance access to private data without exposing companies to competition or privacy concerns. “We need to find ways to access data according to different shades of openness,” Verhulst says….(More)”.

The promise and perils of big gender data


Essay by Bapu Vaitla, Stefaan Verhulst, Linus Bengtsson, Marta C. González, Rebecca Furst-Nichols & Emily Courey Pryor in Special Issue on Big Data of Nature Medicine: “Women and girls are legally and socially marginalized in many countries. As a result, policymakers neglect key gendered issues such as informal labor markets, domestic violence, and mental health1. The scientific community can help push such topics onto policy agendas, but science itself is riven by inequality: women are underrepresented in academia, and gendered research is rarely a priority of funding agencies.

However, the critical importance of better gender data for societal well-being is clear. Mental health is a particularly striking example. Estimates from the Global Burden of Disease database suggest that depressive and anxiety disorders are the second leading cause of morbidity among females between 10 and 63 years of age2. But little is known about the risk factors that contribute to mental illness among specific groups of women and girls, the challenges of seeking care for depression and anxiety, or the long-term consequences of undiagnosed and untreated illness. A lack of data similarly impedes policy action on domestic and intimate-partner violence, early marriage, and sexual harassment, among many other topics.

‘Big data’ can help fill that gap. The massive amounts of information passively generated by electronic devices represent a rich portrait of human life, capturing where people go, the decisions they make, and how they respond to changes in their socio-economic environment. For example, mobile-phone data allow better understanding of health-seeking behavior as well as the dynamics of infectious-disease transmission3. Social-media platforms generate the world’s largest database of thoughts and emotions—information that, if leveraged responsibly, can be used to infer gendered patterns of mental health4. Remote sensors, especially satellites, can be used in conjunction with traditional data sources to increase the spatial and temporal granularity of data on women’s economic activity and health status5.

But the risk of gendered algorithmic bias is a serious obstacle to the responsible use of big data. Data are not value free; they reproduce the conscious and unconscious attitudes held by researchers, programmers, and institutions. Consider, for example, the training datasets on which the interpretation of big data depends. Training datasets establish the association between two or more directly observed phenomena of interest—for example, the mental health of a platform user (typically collected through a diagnostic survey) and the semantic content of the user’s social-media posts. These associations are then used to develop algorithms that interpret big data streams. In the example here, the (directly unobserved) mental health of a large population of social-media users would be inferred from their observed posts….(More)”.

Gendering Smart Mobilities


Book edited by Tanu Priya Uteng, Hilda Rømer Christensen, and Lena Levin: “This book considers gender perspectives on the ‘smart’ turn in urban and transport planning to effectively provide ‘mobility for all’ while simultaneously attending to the goal of creating green and inclusive cities. It deals with the conceptualisation, design, planning, and execution of the fast-emerging ‘smart’ solutions.

The volume questions the efficacy of transformations being brought by smart solutions and highlights the need for a more robust problem formulation to guide the design of smart solutions, and further maps out the need for stronger governance to manage the introduction and proliferation of smart technologies. Authors from a range of disciplinary backgrounds have contributed to this book, designed to converse with mobility studies, transport studies, urban-transport planning, engineering, human geography, sociology, gender studies, and other related fields.

The book fills a substantive gap in the current gender and mobility discourses, and will thus appeal to students and researchers studying mobilities in the social, political, design, technical, and environmental sciences….(More)”.

Big Data, Big Impact? Towards Gender-Sensitive Data Systems


Report by Data2X: “How can insights drawn from big data sources improve understanding about the lives of women and girls?

This question has underpinned Data2X’s groundbreaking work at the intersection of big data and gender — work that funded ten research projects that examined the potential of big data to fill the global gender data gap.

Big Data, Big Impact? Towards Gender-Sensitive Data Systems summarizes the findings and potential policy implications of the Big Data for Gender pilot projects funded by Data2X, and lays out five cross-cutting messages that emerge from this body of work:

  1. Big data offers unique insights on women and girls.
  2. Gender-sensitive big data is ready to scale and integrate with traditional data.
  3. Identify and correct bias in big datasets.
  4. Protect the privacy of women and girls.
  5. Women and girls must be central to data governance.

This report argues that the time for pilot projects has passed. Data privacy concerns must be addressed; investment in scale up is needed. Big data offers great potential for women and girls, and indeed for all people….(More)”.

How does a computer ‘see’ gender?


Pew Research Center: “Machine vision tools like facial recognition are increasingly being used for law enforcement, advertising, and other purposes. Pew Research Center itself recently used a machine vision system to measure the prevalence of men and women in online image search results. This kind of system develops its own rules for identifying men and women after seeing thousands of example images, but these rules can be hard for to humans to discern. To better understand how this works, we showed images of the Center’s staff members to a trained machine vision system similar to the one we used to classify image searches. We then systematically obscured sections of each image to see which parts of the face caused the system to change its decision about the gender of the person pictured. Some of the results seemed intuitive, others baffling. In this interactive challenge, see if you can guess what makes the system change its decision.

Here’s how it works:…(More)”.

Gender Gaps in Urban Mobility


Brief of the Data 2X Big Data and Gender Brief Series by The GovLab, UNICEF, Universidad Del Desarrollo, Telefónica R&D Center, ISI Foundation, and DigitalGlobe: “Mobility is gendered. For example, the household division of labor in many societies leads women and girls to take more multi-purpose, multi-stop trips than men. Women-headed households also tend to work more in the informal sector, with limited access to transportation subsidies, and use of public transit is further reduced by the risk of violence in public spaces.

This brief summarizes a recent analysis of gendered urban mobility in 51 (out of 52) neighborhoods of Santiago, Chile, relying on the call detail records (CDRs) of a large sample of mobile phone users over a period of three months. We found that: 1) women move less overall than men; 2) have a smaller radius of movement; and 3) tend to concentrate their time in a smaller set of locations. These mobility gaps are linked to lower average incomes and fewer public and private transportation options. These insights, taken from large volumes of passively generated, inexpensive data streaming in realtime, can help policymakers design more gender inclusive urban transit systems….(More)”.

Raw data won’t solve our problems — asking the right questions will


Stefaan G. Verhulst in apolitical: “If I had only one hour to save the world, I would spend fifty-five minutes defining the questions, and only five minutes finding the answers,” is a famous aphorism attributed to Albert Einstein.

Behind this quote is an important insight about human nature: Too often, we leap to answers without first pausing to examine our questions. We tout solutions without considering whether we are addressing real or relevant challenges or priorities. We advocate fixes for problems, or for aspects of society, that may not be broken at all.

This misordering of priorities is especially acute — and represents a missed opportunity — in our era of big data. Today’s data has enormous potential to solve important public challenges.

However, policymakers often fail to invest in defining the questions that matter, focusing mainly on the supply side of the data equation (“What data do we have or must have access to?”) rather than the demand side (“What is the core question and what data do we really need to answer it?” or “What data can or should we actually use to solve those problems that matter?”).

As such, data initiatives often provide marginal insights while at the same time generating unnecessary privacy risks by accessing and exploring data that may not in fact be needed at all in order to address the root of our most important societal problems.

A new science of questions

So what are the truly vexing questions that deserve attention and investment today? Toward what end should we strategically seek to leverage data and AI?

The truth is that policymakers and other stakeholders currently don’t have a good way of defining questions or identifying priorities, nor a clear framework to help us leverage the potential of data and data science toward the public good.

This is a situation we seek to remedy at The GovLab, an action research center based at New York University.

Our most recent project, the 100 Questions Initiative, seeks to begin developing a new science and practice of questions — one that identifies the most urgent questions in a participatory manner. Launched last month, the goal of this project is to develop a process that takes advantage of distributed and diverse expertise on a range of given topics or domains so as to identify and prioritize those questions that are high impact, novel and feasible.

Because we live in an age of data and much of our work focuses on the promises and perils of data, we seek to identify the 100 most pressing problems confronting the world that could be addressed by greater use of existing, often inaccessible, datasets through data collaboratives – new forms of cross-disciplinary collaboration beyond public-private partnerships focused on leveraging data for good….(More)”.

Sharing Private Data for Public Good


Stefaan G. Verhulst at Project Syndicate: “After Hurricane Katrina struck New Orleans in 2005, the direct-mail marketing company Valassis shared its database with emergency agencies and volunteers to help improve aid delivery. In Santiago, Chile, analysts from Universidad del Desarrollo, ISI Foundation, UNICEF, and the GovLab collaborated with Telefónica, the city’s largest mobile operator, to study gender-based mobility patterns in order to design a more equitable transportation policy. And as part of the Yale University Open Data Access project, health-care companies Johnson & Johnson, Medtronic, and SI-BONE give researchers access to previously walled-off data from 333 clinical trials, opening the door to possible new innovations in medicine.

These are just three examples of “data collaboratives,” an emerging form of partnership in which participants exchange data for the public good. Such tie-ups typically involve public bodies using data from corporations and other private-sector entities to benefit society. But data collaboratives can help companies, too – pharmaceutical firms share data on biomarkers to accelerate their own drug-research efforts, for example. Data-sharing initiatives also have huge potential to improve artificial intelligence (AI). But they must be designed responsibly and take data-privacy concerns into account.

Understanding the societal and business case for data collaboratives, as well as the forms they can take, is critical to gaining a deeper appreciation the potential and limitations of such ventures. The GovLab has identified over 150 data collaboratives spanning continents and sectors; they include companies such as Air FranceZillow, and Facebook. Our research suggests that such partnerships can create value in three main ways….(More)”.

Journalism Initiative Crowdsources Feedback on Failed Foreign Aid Projects


Abigail Higgins at SSIR: “It isn’t unusual that a girl raped in northeastern Kenya would be ignored by law enforcement. But for Mary, whose name has been changed to protect her identity, it should have been different—NGOs had established a hotline to report sexual violence just a few years earlier to help girls like her get justice. Even though the hotline was backed by major aid institutions like Mercy Corps and the British government, calls to it regularly went unanswered.

“That was the story that really affected me. It touched me in terms of how aid failures could impact someone,” says Anthony Langat, a Nairobi-based reporter who investigated the hotline as part of a citizen journalism initiative called What Went Wrong? that examines failed foreign aid projects.

Over six months in 2018, What Went Wrong? collected 142 reports of failed aid projects in Kenya, each submitted over the phone or via social media by the very people the project was supposed to benefit. It’s a move intended to help upend the way foreign aid is disbursed and debated. Although aid organizations spend significant time evaluating whether or not aid works, beneficiaries are often excluded from that process.

“There’s a serious power imbalance,” says Peter DiCampo, the photojournalist behind the initiative. “The people receiving foreign aid generally do not have much say. They don’t get to choose which intervention they want, which one would feel most beneficial for them. Our goal is to help these conversations happen … to put power into the hands of the people receiving foreign aid.”

What Went Wrong? documented eight failed projects in an investigative series published by Devex in March. In Kibera, one of Kenya’s largest slums, public restrooms meant to improve sanitation failed to connect to water and sewage infrastructure and were later repurposed as churches. In another story, the World Bank and local thugs struggled for control over the slum’s electrical grid….(More)”