Identifying Citizens’ Needs by Combining Artificial Intelligence (AI) and Collective Intelligence (CI)


Report by Andrew Zahuranec, Andrew Young and Stefaan G. Verhulst: “Around the world, public leaders are seeking new ways to better understand the needs of their citizens, and subsequently improve governance, and how we solve public problems. The approaches proposed toward changing public engagement tend to focus on leveraging two innovations. The first involves artificial intelligence (AI), which offers unprecedented abilities to quickly process vast quantities of data to deepen insights into public needs. The second is collective intelligence (CI), which provides means for tapping into the “wisdom of the crowd.” Both have strengths and weaknesses, but little is known on how the combination of both could address their weaknesses while radically transform how we meet public demands for more responsive governance.

Today, The GovLab is releasing a new report, Identifiying Citizens’ Needs By Combining AI and CI, which seeks to identify and assess how institutions might responsibly experiment in how they engage with citizens by leveraging AI and CI together.

The report, authored by Stefaan G. Verhulst, Andrew J. Zahuranec, and Andrew Young, builds upon an initial examination of the intersection of AI and CI conducted in the context of the MacArthur Foundation Research Network on Opening Governance. …

The report features five in-depth case studies and an overview of eight additional examples from around the world on how AI and CI together can help to: 

  • Anticipate citizens’ needs and expectations through cognitive insights and process automation and pre-empt problems through improved forecasting and anticipation;
  • Analyze large volumes of citizen data and feedback, such as identifying patterns in complaints;
  • Allow public officials to create highly personalized campaigns and services; or
  • Empower government service representatives to deliver relevant actions….(More)”.

Three Eras of Digital Governance


Paper by Jonathan L. Zittrain: “To understand where digital governance is going, we must take stock of where it’s been, because the timbre of mainstream thinking around digital governance today is dramatically different than it was when study of “Internet governance” coalesced in the late 1990s.

Perhaps the most obvious change has been from emphasizing networked technologies’ positive effects and promise – couched around concepts like connectivity, innovation, and, by this author, “generativity” – to pointing out their harms and threats. It’s not that threats weren’t previously recognized, but rather that they were more often seen in external clamps on technological development and upon the corresponding new freedoms for users, whether government intervention to block VOIP services like Skype to protect incumbent telco revenues, or in the shaping of technology to effect undue surveillance, whether for government or corporate purposes.

The shift in emphasis from positive to negative corresponds to a change in the overarching frameworks for talking about regulating information technology. We have moved from a discourse around rights – particularly those of end-users, and the ways in which abstention by intermediaries is important to facilitate citizen flourishing – to one of public health, which naturally asks for a weighing of the systemic benefits or harms of a technology, and to think about what systemic interventions might curtail its apparent excesses.

Each framework captures important values around the use of technology that can both empower and limit individual freedom of action, including to engage in harmful conduct. Our goal today should be to identify where competing values frameworks themselves preclude understanding of others’ positions about regulation, and to see if we can map a path forward that, if not reconciling the frameworks, allows for satisfying, if ever-evolving, resolutions to immediate questions of public and private governance…(More)”.

Five Ethical Principles for Humanitarian Innovation


Peter Batali, Ajoma Christopher & Katie Drew in the Stanford Social Innovation Review: “…Based on this experience, UNHCR and CTEN developed a pragmatic, refugee-led, “good enough” approach to experimentation in humanitarian contexts. We believe a wide range of organizations, including grassroots community organizations and big-tech multinationals, can apply this approach to ensure that the people they aim to help hold the reigns of the experimentation process.

1. Collaborate Authentically and Build Intentional Partnerships

Resource and information asymmetry are inherent in the humanitarian system. Refugees have long been constructed as “‘victims”’ in humanitarian response, waiting for “salvation” from heroic humanitarians. Researcher Matthew Zagor describes this construct as follows: “The genuine refugee … is the passive, coerced, patient refugee, the one waiting in the queue—the victim, anticipating our redemptive touch, defined by the very passivity which in our gaze both dehumanizes them, in that they lack all autonomy in our eyes, and romanticizes them as worthy in their potentiality.”

Such power dynamics make authentic collaboration challenging….

2. Avoid Technocratic Language

Communication can divide us or bring us together. Using exclusive or “expert” terminology (terms like “ideation,” “accelerator,” and “design thinking”) or language that reinforces power dynamics or assigns an outsider role (such as “experimenting on”) can alienate community participants. Organizations should aim to use inclusive language than everyone understands, as well as set a positive and realistic tone. Communication should focus on the need to co-develop solutions with the community, and the role that testing or trying something new can play….

3. Don’t Assume Caution Is Best

Research tells us that we feel more regret over actions that lead to negative outcomes than we do over inactions that lead to the same or worse outcomes. As a result, we tend to perceive and weigh action and inaction unequally. So while humanitarian organizations frequently consider the implications of our actions and the possible negative outcome for communities, we don’t always consider the implications of doing nothing. Is it ethical to continue an activity that we know isn’t as effective as it could be, when testing small and learning fast could reap real benefits? In some cases, taking a risk might, in fact, be the least risky path of action. We need to always ask ourselves, “Is it really ethical to do nothing?”…

4. Choose Experiment Participants Based on Values

Many humanitarian efforts identify participants based on their societal role, vulnerability, or other selection criteria. However, these methods often lead to challenges related to incentivization—the need to provide things like tea, transportation, or cash payments to keep participants engaged. Organizations should instead consider identifying participants who demonstrate the values they hope to promote—such as collaboration, transparency, inclusivity, or curiosity. These community members are well-poised to promote inclusivity, model positive behaviors, and engage participants across the diversity of your community….

5. Monitor Community Feedback and Adapt

While most humanitarian agencies know they need to listen and adapt after establishing communication channels, the process remains notoriously challenging. One reason is that community members don’t always share their feedback on experimentation formally; feedback sometimes comes from informal channels or even rumors. Yet consistent, real-time feedback is essential to experimentation. Listening is the pressure valve in humanitarian experimentation; it allows organizations to adjust or stop an experiment if the community flags a negative outcome….(More)”.

The Narrow Corridor


Book by Daron Acemoglu and James A. Robinson: “…In their new book, they build a new theory about liberty and how to achieve it, drawing a wealth of evidence from both current affairs and disparate threads of world history.  

Liberty is hardly the “natural” order of things. In most places and at most times, the strong have dominated the weak and human freedom has been quashed by force or by customs and norms. Either states have been too weak to protect individuals from these threats, or states have been too strong for people to protect themselves from despotism. Liberty emerges only when a delicate and precarious balance is struck between state and society.

There is a Western myth that political liberty is a durable construct, arrived at by a process of “enlightenment.” This static view is a fantasy, the authors argue. In reality, the corridor to liberty is narrow and stays open only via a fundamental and incessant struggle between state and society: The authors look to the American Civil Rights Movement, Europe’s early and recent history, the Zapotec civilization circa 500 BCE, and Lagos’s efforts to uproot corruption and institute government accountability to illustrate what it takes to get and stay in the corridor. But they also examine Chinese imperial history, colonialism in the Pacific, India’s caste system, Saudi Arabia’s suffocating cage of norms, and the “Paper Leviathan” of many Latin American and African nations to show how countries can drift away from it, and explain the feedback loops that make liberty harder to achieve. 

Today we are in the midst of a time of wrenching destabilization. We need liberty more than ever, and yet the corridor to liberty is becoming narrower and more treacherous. The danger on the horizon is not “just” the loss of our political freedom, however grim that is in itself; it is also the disintegration of the prosperity and safety that critically depend on liberty. The opposite of the corridor of liberty is the road to ruin….(More)”.

Why data from companies should be a common good


Paula Forteza at apolitical: “Better planning of public transport, protecting fish from intensive fishing, and reducing the number of people killed in car accidents: for these and many other public policies, data is essential.

Data applications are diverse, and their origins are equally numerous. But data is not exclusively owned by the public sector. Data can be produced by private actors such as mobile phone operators, as part of marine traffic or by inter-connected cars to give just a few examples.

The awareness around the potential of private data is increasing, as the proliferation of data partnerships between companies, governments, local authorities show. However, these partnerships represent only a very small fraction of what could be done.

The opening of public data, meaning that public data is made freely available to everyone, has been conducted on a wide scale in the last 10 years, pioneered by the US and UK, soon followed by France and many other countries. In 2015, France took a first step, as the government introduced the Digital Republic Bill which made data open by default and introduced the concept of public interest data. Due to a broad definition and low enforcement, the opening of private sector data is, nevertheless, still lagging behind.

The main arguments for opening private data are that it will allow better public decision-making and it could trigger a new way to regulate Big Tech. There is, indeed, a strong economic case for data sharing, because data is a non-rival good: the value of data does not diminish when shared. On the contrary, new uses can be designed and data can be enriched by aggregation, which could improve innovation for start-ups….

Why Europe needs a private data act

Data hardly knows any boundaries.

Some states are opening like France did in 2015 by creating a framework for “public interest data,” but the absence of a common international legal framework for private data sharing is a major obstacle to its development. To scale up, a European Private Data Act is needed.

This framework must acknowledge the legitimate interest of the private companies that collect and control data. Data can be their main source of income or one they are wishing to develop, and this must be respected. Trade secrecy has to be protected too: data sharing is not open data.

Data can be shared to a limited and identified number of partners and it does not always have to be free. Yet private interest must be aligned with the public good. The European Convention on Human Rights and the European Charter of Fundamental Rights acknowledge that some legitimate and proportional limitations can be set to the freedom of enterprise, which gives everyone the right to pursue their own profitable business.

The “Private Data Act” should contain several fundamental data sharing principles in line with those proposed by the European Commission in 2018: proportionality, “do no harm”, full respect of the GDPR, etc. It should also include guidelines on which data to share, how to appreciate the public interest, and in which cases data should be opened for free or how the pricing should be set.

Two methods can be considered:

  • Defining high-value datasets, as it has been done for public data in the recent Open Data Directive, in areas like mobile communications, banking, transports, etc. This method is strong but is not flexible enough.
  • Alternatively, governments might define certain “public interest projects”. In doing so, governments could get access to specific data that is seen as a prerequisite to achieve the project. For example, understanding why there is a increasing mortality among bees, requires various data sources: concrete data on bee mortality from the beekeepers, crops and the use of pesticides from the farmers, weather data, etc. This method is more flexible and warrants that only the data needed for the project is shared.

Going ahead on open data and data sharing should be a priority for the upcoming European Commission and Parliament. Margrethe Vestager has been renewed as Competition Commissioner and Vice-President of the Commission and she already mentioned the opportunity to define access to data for newcomers in the digital market.

Public interest data is a new topic on the EU agenda and will probably become crucial in the near future….(More)”.

Andrew Yang proposes that your digital data be considered personal property


Michael Grothaus at Fast Company: “2020 Democratic presidential candidate Andrew Yang may not be at the top of the race when it comes to polling (Politico currently has him ranked as the 7th most-popular Democratic contender), but his policies, including support for universal basic income, have made him popular among a subset of young, liberal-leaning, tech-savvy voters. Yang’s latest proposal, too, is sure to strike a chord with them.

The presidential candidate published his latest policy proposal today: to treat data as a property right. Announcing the proposal on his website, Yang lamented how our data is collected, used, and abused by companies, often with little awareness or consent from us. “This needs to stop,” Yang says. “Data generated by each individual needs to be owned by them, with certain rights conveyed that will allow them to know how it’s used and protect it.”

The rights Yang is proposing:

  • The right to be informed as to what data will be collected, and how it will be used
  • The right to opt out of data collection or sharing
  • The right to be told if a website has data on you, and what that data is
  • The right to be forgotten; to have all data related to you deleted upon request
  • The right to be informed if ownership of your data changes hands
  • The right to be informed of any data breaches including your information in a timely manner
  • The right to download all data in a standardized format to port to another platform…(More)”.

Bottom-up data Trusts: disturbing the ‘one size fits all’ approach to data governance


Sylvie Delacroix and Neil D Lawrence at International Data Privacy Law: “From the friends we make to the foods we like, via our shopping and sleeping habits, most aspects of our quotidian lives can now be turned into machine-readable data points. For those able to turn these data points into models predicting what we will do next, this data can be a source of wealth. For those keen to replace biased, fickle human decisions, this data—sometimes misleadingly—offers the promise of automated, increased accuracy. For those intent on modifying our behaviour, this data can help build a puppeteer’s strings. As we move from one way of framing data governance challenges to another, salient answers change accordingly. Just like the wealth redistribution way of framing those challenges tends to be met with a property-based, ‘it’s our data’ answer, when one frames the problem in terms of manipulation potential, dignity-based, human rights answers rightly prevail (via fairness and transparency-based answers to contestability concerns). Positive data-sharing aspirations tend to be raised within altogether different conversations from those aimed at addressing the above concerns. Our data Trusts proposal challenges these boundaries.

This article proceeds from an analysis of the very particular type of vulnerability concomitant with our ‘leaking’ data on a daily basis, to show that data ownership is both unlikely and inadequate as an answer to the problems at stake. We also argue that the current construction of top-down regulatory constraints on contractual freedom is both necessary and insufficient. To address the particular type of vulnerability at stake, bottom-up empowerment structures are needed. The latter aim to ‘give a voice’ to data subjects whose choices when it comes to data governance are often reduced to binary, ill-informed consent. While the rights granted by instruments like the GDPR can be used as tools in a bid to shape possible data-reliant futures—such as better use of natural resources, medical care, etc, their exercise is both demanding and unlikely to be as impactful when leveraged individually. As a bottom-up governance structure that is uniquely capable of taking into account the vulnerabilities outlined in the first section, we highlight the constructive potential inherent in data Trusts. This potential crosses the traditional boundaries between individualist protection concerns on one hand and collective empowerment aspirations on the other.

The second section explains how the Trust structure allows data subjects to choose to pool the rights they have over their personal data within the legal framework of a data Trust. It is important that there be a variety of data Trusts, arising out of a mix of publicly and privately funded initiatives. Each Trust will encapsulate a particular set of aspirations, reflected in the terms of the Trust. Bound by a fiduciary obligation of undivided loyalty, data trustees will exercise the data rights held under the Trust according to its particular terms. In contrast to a recently commissioned report,1 we explain why data can indeed be held in a Trust, and why the extent to which certain kinds of data may be said to give rise to property rights is neither here nor there as far as our proposal is concerned. What matters, instead, is the extent to which regulatory instruments such as the GDPR confer rights, and for what kind of data. The breadth of those rights will determine the possible scope of data Trusts in various jurisdictions.

Our ‘Case Studies’ aim to illustrate the complementarity of our data Trusts proposal with the legal provisions pertaining to different kinds of personal data, from medical, genetic, financial, and loyalty card data to social media feeds. The final section critically considers a variety of implementation challenges, which range from Trust Law’s cross-jurisdictional aspects to uptake and exit procedures, including issues related to data of shared provenance. We conclude by highlighting the way in which an ecosystem of data Trusts addresses ethical, legal, and political needs that are complementary to those within the reach of regulatory interventions such as the GDPR….(More)”.

Restricting data’s use: A spectrum of concerns in need of flexible approaches


Dharma Akmon and Susan Jekielek at IASSIST Quaterly: “As researchers consider making their data available to others, they are concerned with the responsible use of data. As a result, they often seek to place restrictions on secondary use. The Research Connections archive at ICPSR makes available the datasets of dozens of studies related to childcare and early education. Of the 103 studies archived to date, 20 have some restrictions on access. While ICPSR’s data access systems were designed primarily to accommodate public use data (i.e. data without disclosure concerns) and potentially disclosive data, our interactions with depositors reveal a more nuanced notion range of needs for restricting use. Some data present a relatively low risk of threatening participants’ confidentiality, yet the data producers still want to monitor who is accessing the data and how they plan to use them. Other studies contain data with such a high risk of disclosure that their use must be restricted to a virtual data enclave. Still other studies rest on agreements with participants that require continuing oversight of secondary use by data producers, funders, and participants. This paper describes data producers’ range of needs to restrict data access and discusses how systems can better accommodate these needs….(More)”.

Tracking the Labor Market with “Big Data”


Tomaz Cajner, Leland Crane, Ryan Decker, Adrian Hamins-Puertolas, and Christopher Kurz at FEDSNotes: “Payroll employment growth is one of the most reliable business cycle indicators. Each postwar recession in the United States has been characterized by a year-on-year drop in payroll employment as measured by the BLS Current Employment Statistics (CES) survey, and, outside of these recessionary declines, the year-on-year payroll employment growth has always been positive. Thus, it is not surprising that policymakers, financial markets, and the general public pay a great deal of attention to the CES payroll employment gains reported at the beginning of each month.

However, while the CES survey is one of the most carefully conducted measures of labor market activity and uses an extremely large sample, it is still subject to significant sampling error and nonsampling errors. For example, when the BLS first reported that private nonfarm payroll gains were 148,000 in July 2019, the associated 90 percent confidence interval was +/- 100,000 due to sampling error alone….

One such source of alternative labor market data is the payroll-processing company ADP, which covers 20 percent of the private workforce. These are the data that underlie ADP’s monthly National Employment Report (NER), which forecasts BLS payroll employment changes by using a combination of ADP-derived data and other publicly available data. In our research, we explore the information content of the ADP microdata alone by producing an estimate of employment changes independent from the BLS payroll series as well as from other data sources.

A potential concern when using the ADP data is that only the firms which hire ADP to manage their payrolls will appear in the data, and this may introduce sample selection issues….(More)”

The Economics of Social Data: An Introduction


Paper by Dirk Bergemann and Alessandro Bonatti: “Large internet platforms collect data from individual users in almost every interaction on the internet. Whenever an individual browses a news website, searches for a medical term or for a travel recommendation, or simply checks the weather forecast on an app, that individual generates data. A central feature of the data collected from the individuals is its social aspect. Namely, the data captured from an individual user is not only informative about this specific individual, but also about users in some metric similar to the individual. Thus, the individual data is really social data. The social nature of the data generates an informational externality that we investigate in this note….(More)”.