How Tech Companies Can Advance Data Science for Social Good

Essay by Nick Martin: “As the world struggles to achieve the UN’s Sustainable Development Goals (SDGs), the need for reliable data to track our progress is more important than ever. Government, civil society, and private sector organizations all play a role in producing, sharing, and using this data, but their information-gathering and -analysis efforts have been able to shed light on only 68 percent of the SDG indicators so far, according to a 2019 UN study.

To help fill the gap, the data science for social good (DSSG) movement has for years been making datasets about important social issues—such as health care infrastructure, school enrollment, air quality, and business registrations—available to trusted organizations or the public. Large tech companies such as Facebook, Google, Amazon, and others have recently begun to embrace the DSSG movement. Spurred on by advances in the field, the Development Data Partnership, the World Economic Forum’s 2030Vision consortium, and Data Collaboratives, they’re offering information about social media users’ mobility during COVID-19, cloud computing infrastructure to help nonprofits analyze large datasets, and other important tools and services.

But sharing data resources doesn’t mean they’ll be used effectively, if at all, to advance social impact. High-impact results require recipients of data assistance to inhabit a robust, holistic data ecosystem that includes assets like policies for safely handling data and the skills to analyze it. As tech firms become increasingly involved with using data and data science to help achieve the SDGs, it’s important that they understand the possibilities and limitations of the nonprofits and other civil society organizations they’re working with. Without a firm grasp on the data ecosystems of their partners, all the technical wizardry in the world may be for naught.

Companies must ask questions such as: What incentives or disincentives are in place for nonprofits to experiment with data science in their work? What gaps remain between what nonprofits or data scientists need and the resources funders provide? What skills must be developed? To help find answers, TechChange, an organization dedicated to using technology for social good, partnered with Project17, Facebook’s partnerships-led initiative to accelerate progress on the SDGs. Over the past six months, the team led interviews with top figures in the DSSG community from industry, academia, and the public sector. The 14 experts shared numerous insights into using data and data science to advance social good and the SDGs. Four takeaways emerged from our conversations and research…(More)”.

Algorithmic Colonisation of Africa Read

Abeba Birhane at The Elephant: “The African equivalents of Silicon Valley’s tech start-ups can be found in every possible sphere of life around all corners of the continent—in “Sheba Valley” in Addis Abeba, “Yabacon Valley” in Lagos, and “Silicon Savannah” in Nairobi, to name a few—all pursuing “cutting-edge innovations” in sectors like banking, finance, healthcare, and education. They are headed by technologists and those in finance from both within and outside the continent who seemingly want to “solve” society’s problems, using data and AI to provide quick “solutions”. As a result, the attempt to “solve” social problems with technology is exactly where problems arise. Complex cultural, moral, and political problems that are inherently embedded in history and context are reduced to problems that can be measured and quantified—matters that can be “fixed” with the latest algorithm.

As dynamic and interactive human activities and processes are automated, they are inherently simplified to the engineers’ and tech corporations’ subjective notions of what they mean. The reduction of complex social problems to a matter that can be “solved” by technology also treats people as passive objects for manipulation. Humans, however, far from being passive objects, are active meaning-seekers embedded in dynamic social, cultural, and historical backgrounds.

The discourse around “data mining”, “abundance of data”, and “data-rich continent” shows the extent to which the individual behind each data point is disregarded. This muting of the individual—a person with fears, emotions, dreams, and hopes—is symptomatic of how little attention is given to matters such as people’s well-being and consent, which should be the primary concerns if the goal is indeed to “help” those in need. Furthermore, this discourse of “mining” people for data is reminiscent of the coloniser’s attitude that declares humans as raw material free for the taking. Complex cultural, moral, and political problems that are inherently embedded in history and context are reduced to problems that can be measured and quantified Data is necessarily always about something and never about an abstract entity.

The collection, analysis, and manipulation of data potentially entails monitoring, tracking, and surveilling people. This necessarily impacts people directly or indirectly whether it manifests as change in their insurance premiums or refusal of services. The erasure of the person behind each data point makes it easy to “manipulate behavior” or “nudge” users, often towards profitable outcomes for companies. Considerations around the wellbeing and welfare of the individual user, the long-term social impacts, and the unintended consequences of these systems on society’s most vulnerable are pushed aside, if they enter the equation at all. For companies that develop and deploy AI, at the top of the agenda is the collection of more data to develop profitable AI systems rather than the welfare of individual people or communities. This is most evident in the FinTech sector, one of the prominent digital markets in Africa. People’s digital footprints, from their interactions with others to how much they spend on their mobile top ups, are continually surveyed and monitored to form data for making loan assessments. Smartphone data from browsing history, likes, and locations is recorded forming the basis for a borrower’s creditworthiness.

Artificial Intelligence technologies that aid decision-making in the social sphere are, for the most part, developed and implemented by the private sector whose primary aim is to maximise profit. Protecting individual privacy rights and cultivating a fair society is therefore the least of their concerns, especially if such practice gets in the way of “mining” data, building predictive models, and pushing products to customers. As decision-making of social outcomes is handed over to predictive systems developed by profit-driven corporates, not only are we allowing our social concerns to be dictated by corporate incentives, we are also allowing moral questions to be dictated by corporate interest.

“Digital nudges”, behaviour modifications developed to suit commercial interests, are a prime example. As “nudging” mechanisms become the norm for “correcting” individuals’ behaviour, eating habits, or exercise routines, those developing predictive models are bestowed with the power to decide what “correct” is. In the process, individuals that do not fit our stereotypical ideas of a “fit body”, “good health”, and “good eating habits” end up being punished, outcast, and pushed further to the margins. When these models are imported as state-of-the-art technology that will save money and “leapfrog” the continent into development, Western values and ideals are enforced, either deliberately or intentionally….(More)”.

This app is helping mothers in the Brazilian favelas survive the pandemic

Daniel Avelar at Open Democracy: “As Brazil faces one of the worst COVID-19 outbreaks in the world, a smartphone app is helping residents of impoverished areas known as favelas survive the virus threat amid sudden mass unemployment.

So far, the Latin American country has recorded over 115.000 deaths caused by COVID-19. The shutdown of economic activity wiped out 7.8 million jobs, mostly affecting low-skilled informal workers who form the bulk of the population in the favelas. Emergency income distributed by the government is limited to 60% of the minimum wage, so families are struggling to make ends meet.

Many blame president Jair Bolsonaro for the tragedy. Bolsonaro, a far-right populist, has consistently rallied against science-based policies in the management of the pandemic and pushed for an end to stay-at-home orders. A precocious reopening of the economy is likely to increase infection rates and cause more deaths.

In an attempt to stop the looming humanitarian catastrophe, a coalition of activists in the favelas and corporate partners developed an app that is facilitating the distribution of food and emergency income to thousands of women spearheading families. The app has a facial recognition feature that helps volunteers identify and register recipients of aid and prevents fraud.

So far, the Favela Mothers project has distributed the equivalent to US$ 26 million in food parcels and cash allowances to more than 1.1 million families in 5,000 neighborhoods across the country….(More)”.

Making Open Development Inclusive: Lessons from IDRC Research

Book edited by Matthew L. Smith and Ruhiya Kristine Seward: “A decade ago, a significant trend in using and supporting open practices emerged in international development. “Open development” describes initiatives as wide-ranging as open government and data, open science, open education, and open innovation. The driving theory was that these types of open practices enable more inclusive processes of human development. This volume, drawing on ten years of empirical work and research, analyzes how open development has played out in practice.

Focusing on development practices in the Global South, the contributors assess the crucial questions of who is able to participate and benefit from open practices, and who cannot. Examining a wide range of cases, they offer a macro analysis of how open development ecosystems are governed, and evaluate the inclusiveness of a variety of applications, including creating open educational resources, collaborating in science and knowledge production, and crowdsourcing information….(More)”.

Digital government in developing countries

Essay by Yasodara Córdova and Tiago Peixoto: “According to the World Bank’s Digital Dividends report, fewer than 20 percent of digital government projects are successes. Particularly in developing countries, these numbers are often associated with a number of challenges: limited funding, stretched implementation capacity, and political instability, to name a few. Yet, even in developing countries, despite similar conditions, some projects seem to fare better than others. Why is that? 

The projects we have worked with in the global south have followed a similar pattern. While there were successes, many projects have failed. We have learned a few things along the way, that we think relate directly to the success or failure of digital government projects. These are not scientific conclusions, they’re personal impressions based on what we’ve seen and experienced.   

1. Information first, services afterwards

A basic function of digital government is the provision of actionable information concerning public services, by they online or offline (e.g. opening hours, documents required for services, and so on). Even more so in developing countries, where most public services are in-person, paper-based, and often involve multiple steps. Yet, fueled by international rankings and benchmarks, governments are often eager to skip stages in their digital journey. This leads them to attempt, and often fail, to provide transactional digital services, before they can even learn  how to offer basic information about these services. The first step in effective transformation should be offering information to users in a simple and accessible manner. Done well, that forms a good foundation for the next step: delivering digital services.  

2. Prioritise the things that will make the biggest difference

Remember that public service delivery follows a power law distribution: a small number of services account for the vast majority of transactions with government. Which these services are will vary according to country, level of government, and models of public service delivery. When the time comes to decide where to start, don’t rely on cookie-cutter lists of services to be digitized. Instead, find out which ones are the most used, and will have the greatest impact. Start with the ones that can be delivered faster, and that are most likely to make users’ lives easier. 

3. Don’t digitise the mess

The fact that a process exists doesn’t mean it’s a good process. Transformation is an opportunity to radically rethink how things work. We’ve seen examples including, for instance, requiring multiple copies of a single document, or imposing more procedures on women than men to open a business. When there is inefficiency in a service, map the bottlenecks and think about how to streamline the process. Don’t just digitise the bottlenecks, they will keep on being an expensive problem. Resist the temptation to digitise things that should not exist in the first place. …(More)”.

The Rise of the Data Poor: The COVID-19 Pandemic Seen From the Margins

Essay by Stefania Milan and Emiliano Treré: “Quantification is central to the narration of the COVID-19 pandemic. Numbers determine the existence of the problem and affect our ability to care and contribute to relief efforts. Yet many communities at the margins, including many areas of the Global South, are virtually absent from this number-based narration of the pandemic. This essay builds on critical data studies to warn against the universalization of problems, narratives, and responses to the virus. To this end, it explores two types of data gaps and the corresponding “data poor.” The first gap concerns the data poverty perduring in low-income countries and jeopardizing their ability to adequately respond to the pandemic. The second affects vulnerable populations within a variety of geopolitical and socio-political contexts, whereby data poverty constitutes a dangerous form of invisibility which perpetuates various forms of inequality. But, even during the pandemic, the disempowered manage to create innovative forms of solidarity from below that partially mitigate the negative effects of their invisibility….(More)”.

Digital in the Time of the Coronavirus: Data Science and Technology as a Force for Inclusion

Blog by Aleem Walji: “Crises do not create inequity and fault lines in society, they expose them. The systems and structures that give rise to inequality and inequity are deep-rooted and powerful. In recent months, we have seen the coronavirus bring into high relief many social and economic vulnerabilities across the world. It is now clear that Hispanics and Blacks are even more vulnerable to Covid-19 because of underlying health conditions, more frequent exposure to the virus, and broken social safety nets. This trend will only accelerate as the virus gains a foothold in Africa, parts of Asia, and Latin America.

The impact of the virus in places where health systems are weak, poverty is high, and large numbers of people are immunocompromised could be devastating. How do we mitigate the medium-term and second-order effects of a pandemic that will shrink economic growth and exacerbate inequality? This year alone, more than 500 million people are expected to fall into poverty, mostly in Africa and Asia. To defeat a virus that does not respect geographic boundaries, it is urgent for public and private actors, philanthropies, and global development institutions to use every tool available to alleviate a global humanitarian emergency and attendant economic collapse.

Technology, data science, and digital readiness are crucial elements for an effective emergency response and foundational to sustain a long-term recovery. Already, scientists and researchers across the world are leveraging data and digital platforms to accelerate the development of a vaccine, fast-track clinical trials, and contact tracing using mobile-enabled tools. Sensors are collecting huge amounts of data, and machine learning algorithms are helping policymakers decide when to relax physical distancing and where to open the economy and for how long.

Access to reliable information for decisionmaking, however, is not evenly spread. High frequency, granular, and anonymized datasets are essential for public-health officials and community health workers to target interventions and reach vulnerable populations faster and at a lower cost. Equipped with reliable data, civic technologists can leverage tools like artificial intelligence and machine learning to flatten the curve of Covid-19 and also the curve of inequity and unequal access to services and support.

This will not happen on its own. Preventing a much deeper digital divide will require forward-leaning policymakers, far-sighted investors and grant makers, civic-minded tech innovators and businesses, and a robust, digitally savvy civil society to work collaboratively for social and economic inclusion. It will require political will and improved data governance to deploy digital platforms to serve populations furthest behind. It is in our collective interest to ensure the health and well-being of every segment of society. Digital inclusion is part of the solution.

There are certain pathways public, private and social actors can follow to leverage data science, digital tools, and platforms today….(More)”.

Trade-offs and considerations for the future: Innovation and the COVID-19 response

Essay by Benjamin Kumpf: “…Here are some of the relevant trade-offs I identified. 

Rigour vs. Speed

How to best balance high-quality rigorous research and the need to gain actionable insights rapidly?  

Responding to a pandemic requires working at pace, while investing in ongoing research and the cross-fertilization of disciplines. In our response, we witness the importance of strong networks with academia and DFID’s focus on high-quality research. In parallel, we invest in supporting partners with rapid data collection through methods such as phone surveys, field visits, onsite interviews where possible as well as big data analysis and more. For example, through the International Growth Centre, DFID has supported a Sierra Leone COVID-19 dashboard, providing real time data on current economic conditions and trends from phone–based surveys from 195 towns and villages across Sierra Leone. ….

Breadth vs. depth

How to best balance providing services to large proportions of populations in need, while addressing challenges of specific communities?  

We are seeing emerging evidence that the virus and measures to prevent spread are disproportionately impacting marginalized communities and minorities. For example, in indigenous people are disproportionally affected by the virus in Brazil, Dalits are among the worst affected in India. In development and humanitarian contexts, it is paramount to guide innovation efforts with explicit values, including on the trade-off between scale and addressing last-mile challenges to leaveno–one behind. For example, to facilitate behaviour-change and embed insights from behavioural science and adaptive practices, DFID is supporting the Hygiene Hub, hosted at the London School for Hygiene and Tropical Medicine. The Hub provides free-of-charge advisory services to governments and non-governmental organizations working on COVID-19 related challenges in low and medium-income countries, balancing the need to reach large audiences and to design bespoke interventions for specific communities.  

Exploration vs. adaptation

How to best diversify innovation efforts and investments betweensearching for local solution and adapting proven approaches? 

Adaptive vs. locally-led

How to best learn and adapt, while providing ownership to local players?

Single-point solutions vs. systems-practices

How to advance specific tech and non-tech innovations that address urgent needs, while further improving existing systems? 

Supporting domestic innovators vs. strengthening local solutions and ecosystems

We need explicit conversations to ensure better transparency about this trade-off in innovation investments generally.…(More)”.

Digital inequalities 3.0: Emergent inequalities in the information age

Essay by Laura Robinson et al in FirstMonday: “Marking the 25th anniversary of the “digital divide,” we continue our metaphor of the digital inequality stack by mapping out the rapidly evolving nature of digital inequality using a broad lens. We tackle complex, and often unseen, inequalities spawned by the platform economy, automation, big data, algorithms, cybercrime, cybersafety, gaming, emotional well-being, assistive technologies, civic engagement, and mobility. These inequalities are woven throughout the digital inequality stack in many ways including differentiated access, use, consumption, literacies, skills, and production. While many users are competent prosumers who nimbly work within different layers of the stack, very few individuals are “full stack engineers” able to create or recreate digital devices, networks, and software platforms as pure producers. This new frontier of digital inequalities further differentiates digitally skilled creators from mere users. Therefore, we document emergent forms of inequality that radically diminish individuals’ agency and augment the power of technology creators, big tech, and other already powerful social actors whose dominance is increasing….(More)”

Mapping citizen science contributions to the UN sustainable development goals

Paper by Dilek Frais: “The UN Sustainable Development Goals (SDGs) are a vision for achieving a sustainable future. Reliable, timely, comprehensive, and consistent data are critical for measuring progress towards, and ultimately achieving, the SDGs. Data from citizen science represent one new source of data that could be used for SDG reporting and monitoring. However, information is still lacking regarding the current and potential contributions of citizen science to the SDG indicator framework. Through a systematic review of the metadata and work plans of the 244 SDG indicators, as well as the identification of past and ongoing citizen science initiatives that could directly or indirectly provide data for these indicators, this paper presents an overview of where citizen science is already contributing and could contribute data to the SDG indicator framework.

The results demonstrate that citizen science is “already contributing” to the monitoring of 5 SDG indicators, and that citizen science “could contribute” to 76 indicators, which, together, equates to around 33%. Our analysis also shows that the greatest inputs from citizen science to the SDG framework relate to SDG 15 Life on Land, SDG 11 Sustainable Cities and Communities, SDG 3 Good Health and Wellbeing, and SDG 6 Clean Water and Sanitation. Realizing the full potential of citizen science requires demonstrating its value in the global data ecosystem, building partnerships around citizen science data to accelerate SDG progress, and leveraging investments to enhance its use and impact….(More)”.