Data Collaboratives can transform the way civil society organisations find solutions

Curated on Posted on by Stefaan Verhulst

Stefaan G. Verhulst at Disrupt & Innovate: “The need for innovation is clear: The twenty-first century is shaping up to be one of the most challenging in recent history. From climate change to income inequality to geopolitical upheaval and terrorism: the difficulties confronting International Civil Society Organisations (ICSOs) are unprecedented not only in their variety but also in their complexity. At the same time, today’s practices and tools used by ICSOs seem stale and outdated. Increasingly, it is clear, we need not only new solutions but new methods for arriving at solutions.

Data will likely become more central to meeting these challenges. We live in a quantified era. It is estimated that 90% of the world’s data was generated in just the last two years. We know that this data can help us understand the world in new ways and help us meet the challenges mentioned above. However, we need new data collaboration methods to help us extract the insights from that data.

UNTAPPED DATA POTENTIAL

For all of data’s potential to address public challenges, the truth remains that most data generated today is in fact collected by the private sector – including ICSOs who are often collecting a vast amount of data – such as, for instance, the International Committee of the Red Cross, which generates various (often sensitive) data related to humanitarian activities. This data, typically ensconced in tightly held databases toward maintaining competitive advantage or protecting from harmful intrusion, contains tremendous possible insights and avenues for innovation in how we solve public problems. But because of access restrictions and often limited data science capacity, its vast potential often goes untapped.

DATA COLLABORATIVES AS A SOLUTION

Data Collaboratives offer a way around this limitation. They represent an emerging public-private partnership model, in which participants from different areas — including the private sector, government, and civil society — come together to exchange data and pool analytical expertise.

While still an emerging practice, examples of such partnerships now exist around the world, across sectors and public policy domains. Importantly several ICSOs have started to collaborate with others around their own data and that of the private and public sector. For example:

  • Several civil society organisations, academics, and donor agencies are partnering in the Health Data Collaborative to improve the global data infrastructure necessary to make smarter global and local health decisions and to track progress against the Sustainable Development Goals (SDGs).
  • Additionally, the UN Office for the Coordination of Humanitarian Affairs (UNOCHA) built Humanitarian Data Exchange (HDX), a platform for sharing humanitarian from and for ICSOs – including Caritas, InterAction and others – donor agencies, national and international bodies, and other humanitarian organisations.

These are a few examples of Data Collaboratives that ICSOs are participating in. Yet, the potential for collaboration goes beyond these examples. Likewise, so do the concerns regarding data protection and privacy….(More)”.

The future of statistics and data science

Curated on Posted on by Stefaan Verhulst

Paper by Sofia C. Olhede and Patrick J. Wolfe in Statistics & Probability Letters: “The Danish physicist Niels Bohr is said to have remarked: “Prediction is very difficult, especially about the future”. Predicting the future of statistics in the era of big data is not so very different from prediction about anything else. Ever since we started to collect data to predict cycles of the moon, seasons, and hence future agriculture yields, humankind has worked to infer information from indirect observations for the purpose of making predictions.

Even while acknowledging the momentous difficulty in making predictions about the future, a few topics stand out clearly as lying at the current and future intersection of statistics and data science. Not all of these topics are of a strictly technical nature, but all have technical repercussions for our field. How might these repercussions shape the still relatively young field of statistics? And what can sound statistical theory and methods bring to our understanding of the foundations of data science? In this article we discuss these issues and explore how new open questions motivated by data science may in turn necessitate new statistical theory and methods now and in the future.

Together, the ubiquity of sensing devices, the low cost of data storage, and the commoditization of computing have led to a volume and variety of modern data sets that would have been unthinkable even a decade ago. We see four important implications for statistics.

First, many modern data sets are related in some way to human behavior. Data might have been collected by interacting with human beings, or personal or private information traceable back to a given set of individuals might have been handled at some stage. Mathematical or theoretical statistics traditionally does not concern itself with the finer points of human behavior, and indeed many of us have only had limited training in the rules and regulations that pertain to data derived from human subjects. Yet inevitably in a data-rich world, our technical developments cannot be divorced from the types of data sets we can collect and analyze, and how we can handle and store them.

Second, the importance of data to our economies and civil societies means that the future of regulation will look not only to protect our privacy, and how we store information about ourselves, but also to include what we are allowed to do with that data. For example, as we collect high-dimensional vectors about many family units across time and space in a given region or country, privacy will be limited by that high-dimensional space, but our wish to control what we do with data will go beyond that….

Third, the growing complexity of algorithms is matched by an increasing variety and complexity of data. Data sets now come in a variety of forms that can be highly unstructured, including images, text, sound, and various other new forms. These different types of observations have to be understood together, resulting in multimodal data, in which a single phenomenon or event is observed through different types of measurement devices. Rather than having one phenomenon corresponding to single scalar values, a much more complex object is typically recorded. This could be a three-dimensional shape, for example in medical imaging, or multiple types of recordings such as functional magnetic resonance imaging and simultaneous electroencephalography in neuroscience. Data science therefore challenges us to describe these more complex structures, modeling them in terms of their intrinsic patterns.

Finally, the types of data sets we now face are far from satisfying the classical statistical assumptions of identically distributed and independent observations. Observations are often “found” or repurposed from other sampling mechanisms, rather than necessarily resulting from designed experiments….

 Our field will either meet these challenges and become increasingly ubiquitous, or risk rapidly becoming irrelevant to the future of data science and artificial intelligence….(More)”.

Data journalism and the ethics of publishing Twitter data

Curated on Posted on by Stefaan Verhulst

Matthew L. Williams at Data Driven Journalism: “Collecting and publishing data collected from social media sites such as Twitter are everyday practices for the data journalist. Recent findings from Cardiff University’s Social Data Science Lab question the practice of publishing Twitter content without seeking some form of informed consent from users beforehand. Researchers found that tweets collected around certain topics, such as those related to terrorism, political votes, changes in the law and health problems, create datasets that might contain sensitive content, such as extreme political opinion, grossly offensive comments, overly personal revelations and threats to life (both to oneself and to others). Handling these data in the process of analysis (such as classifying content as hateful and potentially illegal) and reporting has brought the ethics of using social media in social research and journalism into sharp focus.

Ethics is an issue that is becoming increasingly salient in research and journalism using social media data. The digital revolution has outpaced parallel developments in research governance and agreed good practice. Codes of ethical conduct that were written in the mid twentieth century are being relied upon to guide the collection, analysis and representation of digital data in the twenty-first century. Social media is particularly ethically challenging because of the open availability of the data (particularly from Twitter). Many platforms’ terms of service specifically state users’ data that are public will be made available to third parties, and by accepting these terms users legally consent to this. However, researchers and data journalists must interpret and engage with these commercially motivated terms of service through a more reflexive lens, which implies a context sensitive approach, rather than focusing on the legally permissible uses of these data.

Social media researchers and data journalists have experimented with data from a range of sources, including Facebook, YouTube, Flickr, Tumblr and Twitter to name a few. Twitter is by far the most studied of all these networks. This is because Twitter differs from other networks, such as Facebook, that are organised around groups of ‘friends’, in that it is more ‘open’ and the data (in part) are freely available to researchers. This makes Twitter a more public digital space that promotes the free exchange of opinions and ideas. Twitter has become the primary space for online citizens to publicly express their reaction to events of national significance, and also the primary source of data for social science research into digital publics.

The Twitter streaming API provides three levels of data access: the free random 1% that provides ~5M tweets daily and the random 10% and 100% (chargeable or free to academic researchers upon request). Datasets on social interactions of this scale, speed and ease of access have been hitherto unrealisable in the social sciences and journalism, and have led to a flood of journal articles and news pieces, many of which include tweets with full text content and author identity without informed consent. This is presumably because of Twitter’s ‘open’ nature, which leads to the assumption that ‘these are public data’ and using it does not require the rigor and scrutiny of an ethical oversight. Even when these data are scrutinised, journalists don’t need to be convinced by the ‘public data’ argument, due to the lack of a framework to evaluate the potential harms to users. The Social Data Science Lab takes a more ethically reflexive approach to the use of social media data in social research, and carefully considers users’ perceptions, online context and the role of algorithms in estimating potentially sensitive user characteristics.

A recent Lab survey conducted into users’ perceptions of the use of their social media posts found the following:

  • 94% were aware that social media companies had Terms of Service
  • 65% had read the Terms of Service in whole or in part
  • 76% knew that when accepting Terms of Service they were giving permission for some of their information to be accessed by third parties
  • 80% agreed that if their social media information is used in a publication they would expect to be asked for consent
  • 90% agreed that if their tweets were used without their consent they should be anonymized…(More)”.

Solving Public Problems with Data

Curated on Posted on by Stefaan Verhulst

Dinorah Cantú-Pedraza and Sam DeJohn at The GovLab: “….To serve the goal of more data-driven and evidence-based governing,  The GovLab at NYU Tandon School of Engineering this week launched “Solving Public Problems with Data,” a new online course developed with support from the Laura and John Arnold Foundation.

This online lecture series helps those working for the public sector, or simply in the public interest, learn to use data to improve decision-making. Through real-world examples and case studies — captured in 10 video lectures from leading experts in the field — the new course outlines the fundamental principles of data science and explores ways practitioners can develop a data analytical mindset. Lectures in the series include:

  1. Introduction to evidence-based decision-making  (Quentin Palfrey, formerly of MIT)
  2. Data analytical thinking and methods, Part I (Julia Lane, NYU)
  3. Machine learning (Gideon Mann, Bloomberg LP)
  4. Discovering and collecting data (Carter Hewgley, Johns Hopkins University)
  5. Platforms and where to store data (Arnaud Sahuguet, Cornell Tech)
  6. Data analytical thinking and methods, Part II (Daniel Goroff, Alfred P. Sloan Foundation)
  7. Barriers to building a data practice (Beth Blauer, Johns Hopkins University and GovEx)
  8. Data collaboratives (Stefaan G. Verhulst, The GovLab)
  9. Strengthening a data analytic culture (Amen Ra Mashariki, ESRI)
  10. Data governance and sharing (Beth Simone Noveck, NYU Tandon/The GovLab)

The goal of the lecture series is to enable participants to define and leverage the value of data to achieve improved outcomes and equities, reduced cost and increased efficiency in how public policies and services are created. No prior experience with computer science or statistics is necessary or assumed. In fact, the course is designed precisely to serve public professionals seeking an introduction to data science….(More)”.

Democracy is dead: long live democracy!

Curated on Posted on by Stefaan Verhulst

Helen Margetts in OpenDemocracy: “In the course of the World Forum for Democracy 2017, and in political commentary more generally, social media are blamed for almost everything that is wrong with democracy. They are held responsible for pollution of the democratic environment through fake news, junk science, computational propaganda and aggressive micro-targeting. In turn, these phenomena have been blamed for the rise of populism, political polarization, far-right extremism and radicalisation, waves of hate against women and minorities, post-truth, the end of representative democracy, fake democracy and ultimately, the death of democracy. It feels like the tirade of relatives of the deceased at the trial of the murderer. It is extraordinary how much of this litany is taken almost as given, the most gloomy prognoses as certain visions of the future.

Yet actually we know rather little about the relationship between social media and democracy. Because ten years of the internet and social media have challenged everything we thought we knew.  They have injected volatility and instability into political systems, bringing a continual cast of unpredictable events. They bring into question normative models of democracy – by which we might understand the macro-level shifts at work  – seeming to make possible the highest hopes and worst fears of republicanism and pluralism.

They have transformed the ecology of interest groups and mobilizations. They have challenged élites and ruling institutions, bringing regulatory decay and policy sclerosis. They create undercurrents of political life that burst to the surface in seemingly random ways, making fools of opinion polls and pollsters. And although the platforms themselves generate new sources of real-time transactional data that might be used to understand and shape this changed environment, most of this data is proprietary and inaccessible to researchers, meaning that the revolution in big data and data science has passed by democracy research.

What do we know? The value of tiny acts

Certainly digital media are entwined with every democratic institution and the daily lives of citizens. When deciding whether to vote, to support, to campaign, to demonstrate, to complain – digital media are with us at every step, shaping our information environment and extending our social networks by creating hundreds or thousands of ‘weak ties’, particularly for users of social media platforms such as Facebook or Instagram….(More)”.

When Data Science Destabilizes Democracy and Facilitates Genocide

Curated on Posted on by Stefaan Verhulst

Rachel Thomas in Fast.AI on “What is the ethical responsibility of data scientists?”…What we’re talking about is a cataclysmic change… What we’re talking about is a major foreign power with sophistication and ability to involve themselves in a presidential election and sow conflict and discontent all over this country… You bear this responsibility. You’ve created these platforms. And now they are being misused, Senator Feinstein said this week in a senate hearing. Who has created a cataclysmic change? Who bears this large responsibility? She was talking to executives at tech companies and referring to the work of data scientists.

Data science can have a devastating impact on our world, as illustrated by inflammatory Russian propaganda being shown on Facebook to 126 million Americans leading up to the 2016 election (and the subject of the senate hearing described above) or by lies spread via Facebook that are fueling ethnic cleansing in Myanmar. Over half a million Rohinyga have been driven from their homes due to systematic murder, rape, and burning. Data science is foundational to Facebook’s newsfeed, in determining what content is prioritized and who sees what….

The examples of bias in data science are myriad and include:

You can do awesome and meaningful things with data science (such as diagnosing cancer, stopping deforestation, increasing farm yields, and helping patients with Parkinson’s disease), and you can (often unintentionally) enable terrible things with data science, as the examples in this post illustrate. Being a data scientist entails both great opportunity, as well as great responsibility, to use our skills to not make the world a worse place. Ultimately, doing data science is about humans, not just the users of our products, but everyone who will be impacted by our work. (More)”.

Understanding Corporate Data Sharing Decisions: Practices, Challenges, and Opportunities for Sharing Corporate Data with Researchers

Curated on Posted on by Stefaan Verhulst

Leslie Harris at the Future of Privacy Forum: “Data has become the currency of the modern economy. A recent study projects the global volume of data to grow from about 0.8 zettabytes (ZB) in 2009 to more than 35 ZB in 2020, most of it generated within the last two years and held by the corporate sector.

As the cost of data collection and storage becomes cheaper and computing power increases, so does the value of data to the corporate bottom line. Powerful data science techniques, including machine learning and deep learning, make it possible to search, extract and analyze enormous sets of data from many sources in order to uncover novel insights and engage in predictive analysis. Breakthrough computational techniques allow complex analysis of encrypted data, making it possible for researchers to protect individual privacy, while extracting valuable insights.

At the same time, these newfound data sources hold significant promise for advancing scholarship and shaping more impactful social policies, supporting evidence-based policymaking and more robust government statistics, and shaping more impactful social interventions. But because most of this data is held by the private sector, it is rarely available for these purposes, posing what many have argued is a serious impediment to scientific progress.

A variety of reasons have been posited for the reluctance of the corporate sector to share data for academic research. Some have suggested that the private sector doesn’t realize the value of their data for broader social and scientific advancement. Others suggest that companies have no “chief mission” or public obligation to share. But most observers describe the challenge as complex and multifaceted. Companies face a variety of commercial, legal, ethical, and reputational risks that serve as disincentives to sharing data for academic research, with privacy – particularly the risk of reidentification – an intractable concern. For companies, striking the right balance between the commercial and societal value of their data, the privacy interests of their customers, and the interests of academics presents a formidable dilemma.

To be sure, there is evidence that some companies are beginning to share for academic research. For example, a number of pharmaceutical companies are now sharing clinical trial data with researchers, and a number of individual companies have taken steps to make data available as well. What is more, companies are also increasingly providing open or shared data for other important “public good” activities, including international development, humanitarian assistance and better public decision-making. Some are contributing to data collaboratives that pool data from different sources to address societal concerns. Yet, it is still not clear whether and to what extent this “new era of data openness” will accelerate data sharing for academic research.

Today, the Future of Privacy Forum released a new study, Understanding Corporate Data Sharing Decisions: Practices, Challenges, and Opportunities for Sharing Corporate Data with Researchers. In this report, we aim to contribute to the literature by seeking the “ground truth” from the corporate sector about the challenges they encounter when they consider making data available for academic research. We hope that the impressions and insights gained from this first look at the issue will help formulate further research questions, inform the dialogue between key stakeholders, and identify constructive next steps and areas for further action and investment….(More)”.

Decoding the Social World: Data Science and the Unintended Consequences of Communication

Curated on Posted on by Stefaan Verhulst

Book by Sandra González-Bailón: “Social life is full of paradoxes. Our intentional actions often trigger outcomes that we did not intend or even envision. How do we explain those unintended effects and what can we do to regulate them? In Decoding the Social World, Sandra González-Bailón explains how data science and digital traces help us solve the puzzle of unintended consequences—offering the solution to a social paradox that has intrigued thinkers for centuries. Communication has always been the force that makes a collection of people more than the sum of individuals, but only now can we explain why: digital technologies have made it possible to parse the information we generate by being social in new, imaginative ways. And yet we must look at that data, González-Bailón argues, through the lens of theories that capture the nature of social life. The technologies we use, in the end, are also a manifestation of the social world we inhabit.

González-Bailón discusses how the unpredictability of social life relates to communication networks, social influence, and the unintended effects that derive from individual decisions. She describes how communication generates social dynamics in aggregate (leading to episodes of “collective effervescence”) and discusses the mechanisms that underlie large-scale diffusion, when information and behavior spread “like wildfire.” She applies the theory of networks to illuminate why collective outcomes can differ drastically even when they arise from the same individual actions. By opening the black box of unintended effects, González-Bailón identifies strategies for social intervention and discusses the policy implications—and how data science and evidence-based research embolden critical thinking in a world that is constantly changing….(More)”.

Cross-sector Collaboration in Data Science for Social Good: Opportunities, Challenges, and Open Questions Raised by Working with Academic Researchers

Curated on Posted on by Stefaan Verhulst

Paper by presented by Anissa Tanweer and Brittany Fiore-Gartland at the Data Science for Social Good Conference: “Recent years have seen growing support for attempts to solve complex social problems through the use of increasingly available, increasingly combinable, and increasingly computable digital data. Sometimes referred to as “data science for social good” (DSSG), these efforts are not concentrated in the hands of any one sector of society. Rather, we see DSSG emerging as an inherently multi-sector and collaborative phenomenon, with key participants hailing from governments, nonprofit organizations, technology companies, and institutions of higher education. Based on three years of participant observation in a university-hosted DSSG program, in this paper we highlight academic contributions to multi-sector DSSG collaborations, including expertise, labor, ethics, experimentation, and neutrality. After articulating both the opportunities and challenges that accompany those contributions, we pose some key open questions that demand attention from participants in DSSG programs and projects. Given the emergent nature of the DSSG phenomenon, it is our contention that how these questions come to be answered will have profound implications for the way society is organized and governed….(More)”.

Ethical Guidelines for Applying Predictive Tools Within Human Services

Curated on Posted on by Stefaan Verhulst

MetroLab Network: “Predictive analytical tools are already being put to work within human service agencies to help make vital decisions about when and how to intervene in the lives of families and communities. The sector may not be entirely comfortable with this trend, but it should not be surprised. Predictive models are in wide use within the justice and education sectors and, more to the point, they work: risk assessment is fundamental to what social services do, and these tools can help agencies respond more quickly to prevent harm, to create more personalized interventions, and allocate scarce public resources to where they can do the most good.

There is also a strong case that predictive risk models (PRM) can reduce bias in decision-making. Designing a predictive model forces more explicit conversations about how agencies think about different risk factors and how they propose to guard against disadvantaging certain demographic or socioeconomic groups. And the standard that agencies are trying to improve upon is not perfect equity—it is the status quo, which is neither transparent nor uniformly fair. Risk scores do not eliminate the possibility of personal or institutional prejudice but they can make it more apparent by providing a common reference point.

That the use of predictive analytics in social services can reduce bias is not to say that it will. Careless or unskilled development of these predictive tools could worsen disparities among clients receiving social services. Child and civil rights advocates rightly worry about the potential for “net widening”—drawing more people in for unnecessary scrutiny by the government. They worry that rather than improving services for vulnerable clients, these models will replicate the biases in existing public data sources and expose them to greater trauma. Bad models scale just as quickly as good ones, and even the best of them can be misused.

The stakes here are real: for children and families that interact with these social systems and for the reputation of the agencies that turn to these tools. What, then, should a public leader know about risk modeling, and what lessons does it offer about how to think about data science, data stewardship, and the public interest?…(More)”.