Selected Readings on Algorithmic Scrutiny


By Prianka Srinivasan, Andrew Young and Stefaan Verhulst

The Living Library’s Selected Readings series seeks to build a knowledge base on innovative approaches for improving the effectiveness and legitimacy of governance. This curated and annotated collection of recommended works on the topic of algorithmic scrutiny was originally published in 2017.

Introduction

From government policy, to criminal justice, to our news feeds; to business and consumer practices, the processes that shape our lives both online and off are more and more driven by data and the complex algorithms used to form rulings or predictions. In most cases, these algorithms have created “black boxes” of decision making, where models remain inscrutable and inaccessible. It should therefore come as no surprise that several observers and policymakers are calling for more scrutiny of how algorithms are designed and work, particularly when their outcomes convey intrinsic biases or defy existing ethical standards.

While the concern about values in technology design is not new, recent developments in machine learning, artificial intelligence and the Internet of Things have increased the urgency to establish processes and develop tools to scrutinize algorithms.

In what follows, we have curated several readings covering the impact of algorithms on:

  • Information Intermediaries;
  • Governance
  • Finance
  • Justice

In addition we have selected a few readings that provide insight on possible processes and tools to establish algorithmic scrutiny.

Selected Reading List

Information Intermediaries

Governance

Consumer Finance

Justice

Tools & Process Toward Algorithmic Scrutiny

Annotated Selected Reading List

Information Intermediaries

Diakopoulos, Nicholas. “Algorithmic accountability: Journalistic investigation of computational power structures.” Digital Journalism 3.3 (2015): 398-415. http://bit.ly/.

  • This paper attempts to substantiate the notion of accountability for algorithms, particularly how they relate to media and journalism. It puts forward the notion of “algorithmic power,” analyzing the framework of influence such systems exert, and also introduces some of the challenges in the practice of algorithmic accountability, particularly for computational journalists.
  • Offers a basis for how algorithms can be analyzed, built in terms of the types of decisions algorithms make in prioritizing, classifying, associating, and filtering information.

Diakopoulos, Nicholas, and Michael Koliska. “Algorithmic transparency in the news media.” Digital Journalism (2016): 1-20. http://bit.ly/2hMvXdE.

  • This paper analyzes the increased use of “computational journalism,” and argues that though transparency remains a key tenet of journalism, the use of algorithms in gathering, producing and disseminating news undermines this principle.
  • It first analyzes what the ethical principle of transparency means to journalists and the media. It then highlights the findings from a focus-group study, where 50 participants from the news media and academia were invited to discuss three different case studies related to the use of algorithms in journalism.
  • They find two key barriers to algorithmic transparency in the media: “(1) a lack of business incentives for disclosure, and (2) the concern of overwhelming end-users with too much information.”
  • The study also finds a variety of opportunities for transparency across the “data, model, inference, and interface” components of an algorithmic system.

Napoli, Philip M. “The algorithm as institution: Toward a theoretical framework for automated media production and consumption.” Fordham University Schools of Business Research Paper (2013). http://bit.ly/2hKBHqo

  • This paper puts forward an analytical framework to discuss the algorithmic content creation of media and journalism in an attempt to “close the gap” on theory related to automated media production.
  • By borrowing concepts from institutional theory, the paper finds that algorithms are distinct forms of media institutions, and the cultural and political implications of this interpretation.
  • It urges further study in the field of “media sociology” to further unpack the influence of algorithms, and their role in institutionalizing certain norms, cultures and ways of thinking.

Introna, Lucas D., and Helen Nissenbaum. “Shaping the Web: Why the politics of search engines matters.” The Information Society 16.3 (2000): 169-185. http://bit.ly/2ijzsrg.

  • This paper, published 16 years ago, provides an in-depth account of some of the risks related to search engine optimizations, and the biases and harms these can introduce, particularly on the nature of politics.
  • Suggests search engines can be designed to account for these political dimensions, and better correlate with the ideal of the World Wide Web as being a place that is open, accessible and democratic.
  • According to the paper, policy (and not the free market) is the only way to spur change in this field, though the current technical solutions we have introduce further challenges.

Gillespie, Tarleton. “The Relevance of Algorithms.” Media
technologies: Essays on communication, materiality, and society (2014): 167. http://bit.ly/2h6ASEu.

  • This paper suggests that the extended use of algorithms, to the extent that they undercut many aspects of our lives, (Tarleton calls this public relevance algorithms) are fundamentally “producing and certifying knowledge.” In this ability to create a particular “knowledge logic,” algorithms are a primary feature of our information ecosystem.
  • The paper goes on to map 6 dimensions of these public relevance algorithms:
    • Patterns of inclusion
    • Cycles of anticipation
    • The evaluation of relevance
    • The promise of algorithmic objectivity
    • Entanglement with practice
    • The production of calculated publics
  • The paper concludes by highlighting the need for a sociological inquiry into the function, implications and contexts of algorithms, and to “soberly  recognize their flaws and fragilities,” despite the fact that much of their inner workings remain hidden.

Rainie, Lee and Janna Anderson. “Code-Dependent: Pros and Cons of the Algorithm Age.” Pew Research Center. February 8, 2017. http://bit.ly/2kwnvCo.

  • This Pew Research Center report examines the benefits and negative impacts of algorithms as they become more influential in different sectors and aspects of daily life.
  • Through a scan of the research and practice, with a particular focus on the research of experts in the field, Rainie and Anderson identify seven key themes of the burgeoning Algorithm Age:
    • Algorithms will continue to spread everywhere
    • Good things lie ahead
    • Humanity and human judgment are lost when data and predictive modeling become paramount
    • Biases exist in algorithmically-organized systems
    • Algorithmic categorizations deepen divides
    • Unemployment will rise; and
    • The need grows for algorithmic literacy, transparency and oversight

Tufekci, Zeynep. “Algorithmic harms beyond Facebook and Google: Emergent challenges of computational agency.” Journal on Telecommunications & High Technology Law 13 (2015): 203. http://bit.ly/1JdvCGo.

  • This paper establishes some of the risks and harms in regard to algorithmic computation, particularly in their filtering abilities as seen in Facebook and other social media algorithms.
  • Suggests that the editorial decisions performed by algorithms can have significant influence on our political and cultural realms, and categorizes the types of harms that algorithms may have on individuals and their society.
  • Takes two case studies–one from the social media coverage of the Ferguson protests, the other on how social media can influence election turnouts–to analyze the influence of algorithms. In doing so, this paper lays out the “tip of the iceberg” in terms of some of the challenges and ethical concerns introduced by algorithmic computing.

Mittelstadt, Brent, Patrick Allo, Mariarosaria Taddeo, Sandra Wachter, and Luciano Floridi. “The Ethics of Algorithms: Mapping the Debate.” Big Data & Society (2016): 3(2). http://bit.ly/2kWNwL6

  • This paper provides significant background and analysis of the ethical context of algorithmic decision-making. It primarily seeks to map the ethical consequences of algorithms, which have adopted the role of a mediator between data and action within societies.
  • Develops a conceptual map of 6 ethical concerns:
      • Inconclusive Evidence
      • Inscrutable Evidence
      • Misguided Evidence
      • Unfair Outcomes
      • Transformative Effects
    • Traceability
  • The paper then reviews existing literature, which together with the map creates a structure to inform future debate.

Governance

Janssen, Marijn, and George Kuk. “The challenges and limits of big data algorithms in technocratic governance.” Government Information Quarterly 33.3 (2016): 371-377. http://bit.ly/2hMq4z6.

  • In regarding the centrality of algorithms in enforcing policy and extending governance, this paper analyzes the “technocratic governance” that has emerged by the removal of humans from decision making processes, and the inclusion of algorithmic automation.
  • The paper argues that the belief in technocratic governance producing neutral and unbiased results, since their decision-making processes are uninfluenced by human thought processes, is at odds with studies that reveal the inherent discriminatory practices that exist within algorithms.
  • Suggests that algorithms are still bound by the biases of designers and policy-makers, and that accountability is needed to improve the functioning of an algorithm. In order to do so, we must acknowledge the “intersecting dynamics of algorithm as a sociotechnical materiality system involving technologies, data and people using code to shape opinion and make certain actions more likely than others.”

Just, Natascha, and Michael Latzer. “Governance by algorithms: reality construction by algorithmic selection on the Internet.” Media, Culture & Society (2016): 0163443716643157. http://bit.ly/2h6B1Yv.

  • This paper provides a conceptual framework on how to assess the governance potential of algorithms, asking how technology and software governs individuals and societies.
  • By understanding algorithms as institutions, the paper suggests that algorithmic governance puts in place more evidence-based and data-driven systems than traditional governance methods. The result is a form of governance that cares more about effects than causes.
  • The paper concludes by suggesting that algorithmic selection on the Internet tends to shape individuals’ realities and social orders by “increasing individualization, commercialization, inequalities, deterritorialization, and decreasing transparency, controllability, predictability.”

Consumer Finance

Hildebrandt, Mireille. “The dawn of a critical transparency right for the profiling era.” Digital Enlightenment Yearbook 2012 (2012): 41-56. http://bit.ly/2igJcGM.

  • Analyzes the use of consumer profiling by online businesses in order to target marketing and services to their needs. By establishing how this profiling relates to identification, the author also offers some of the threats to democracy and the right of autonomy posed by these profiling algorithms.
  • The paper concludes by suggesting that cross-disciplinary transparency is necessary to design more accountable profiling techniques that can match the extension of “smart environments” that capture ever more data and information from users.

Reddix-Smalls, Brenda. “Credit Scoring and Trade Secrecy: An Algorithmic Quagmire or How the Lack of Transparency in Complex Financial Models Scuttled the Finance Market.” UC Davis Business Law Journal 12 (2011): 87. http://bit.ly/2he52ch

  • Analyzes the creation of predictive risk models in financial markets through algorithmic systems, particularly in regard to credit scoring. It suggests that these models were corrupted in order to maintain a competitive market advantage: “The lack of transparency and the legal environment led to the use of these risk models as predatory credit pricing instruments as opposed to accurate credit scoring predictive instruments.”
  • The paper suggests that without greater transparency of these financial risk model, and greater regulation over their abuse, another financial crisis like that in 2008 is highly likely.

Justice

Aas, Katja Franko. “Sentencing Transparency in the Information Age.” Journal of Scandinavian Studies in Criminology and Crime Prevention 5.1 (2004): 48-61. http://bit.ly/2igGssK.

  • This paper questions the use of predetermined sentencing in the US judicial system through the application of computer technology and sentencing information systems (SIS). By assessing the use of these systems between the English speaking world and Norway, the author suggests that such technological approaches to sentencing attempt to overcome accusations of mistrust, uncertainty and arbitrariness often leveled against the judicial system.
  • However, in their attempt to rebuild trust, such technological solutions can be seen as an attempt to remedy a flawed view of judges by the public. Therefore, the political and social climate must be taken into account when trying to reform these sentencing systems: “The use of the various sentencing technologies is not only, and not primarily, a matter of technological development. It is a matter of a political and cultural climate and the relations of trust in a society.”

Cui, Gregory. “Evidence-Based Sentencing and the Taint of Dangerousness.” Yale Law Journal Forum 125 (2016): 315-315. http://bit.ly/1XLAvhL.

  • This short essay submitted on the Yale Law Journal Forum calls for greater scrutiny of “evidence based sentencing,” where past data is computed and used to predict future criminal behavior of a defendant. The author suggests that these risk models may undermine the Constitution’s prohibition of bills of attainder, and also are unlawful for inflicting punishment without a judicial trial.

Tools & Processes Toward Algorithmic Scrutiny

Ananny, Mike and Crawford, Kate. “Seeing without knowing: Limitations of the transparency ideal and its application to algorithmic accountability.” New Media & Society. SAGE Publications. 2016. http://bit.ly/2hvKc5x.

  • This paper attempts to critically analyze calls to improve the transparency of algorithms, asking how historically we are able to confront the limitations of the transparency ideal in computing.
  • By establishing “transparency as an ideal” the paper tracks the philosophical and historical lineage of this principle, attempting to establish what laws and provisions were put in place across the world to keep up with and enforce this ideal.
  • The paper goes on to detail the limits of transparency as an ideal, arguing, amongst other things, that it does not necessarily build trust, it privileges a certain function (seeing) over others (say, understanding) and that it has numerous technical limitations.
  • The paper ends by concluding that transparency is an inadequate way to govern algorithmic systems, and that accountability must acknowledge the ability to govern across systems.

Datta, Anupam, Shayak Sen, and Yair Zick. “Algorithmic Transparency via Quantitative Input Influence.Proceedings of 37th IEEE Symposium on Security and Privacy. 2016. http://bit.ly/2hgyLTp.

  • This paper develops what is called a family of Quantitative Input Influence (QII) measures “that capture the degree of influence of inputs on outputs of systems.” The attempt is to theorize a transparency report that is to accompany any algorithmic decisions made, in order to explain any decisions and detect algorithmic discrimination.
  • QII works by breaking “correlations between inputs to allow causal reasoning, and computes the marginal influence of inputs in situations where inputs cannot affect outcomes alone.”
  • Finds that these QII measures are useful in scrutinizing algorithms when “black box” access is available.

Goodman, Bryce, and Seth Flaxman. “European Union regulations on algorithmic decision-making and a right to explanationarXiv preprint arXiv:1606.08813 (2016). http://bit.ly/2h6xpWi.

  • This paper analyzes the implications of a new EU law, to be enacted in 2018, that calls to “restrict automated individual decision-making (that is, algorithms that make decisions based on user level predictors) which ‘significantly affect’ users.” The law will also allow for a “right to explanation” where users can ask for an explanation behind automated decision made about them.
  • The paper, while acknowledging the challenges in implementing such laws, suggests that such regulations can spur computer scientists to create algorithms and decision making systems that are more accountable, can provide explanations, and do not produce discriminatory results.
  • The paper concludes by stating algorithms and computer systems should not aim to be simply efficient, but also fair and accountable. It is optimistic about the ability to put in place interventions to account for and correct discrimination.

Kizilcec, René F. “How Much Information?: Effects of Transparency on Trust in an Algorithmic Interface.” Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems. ACM, 2016. http://bit.ly/2hMjFUR.

  • This paper studies how transparency of algorithms affects our impression of trust by conducting an online field experiment, where participants enrolled in a MOOC a given different explanations for the computer generated grade given in their class.
  • The study found that “Individuals whose expectations were violated (by receiving a lower grade than expected) trusted the system less, unless the grading algorithm was made more transparent through explanation. However, providing too much information eroded this trust.”
  • In conclusion, the study found that a balance of transparency was needed to maintain trust amongst the participants, suggesting that pure transparency of algorithmic processes and results may not correlate with high feelings of trust amongst users.

Kroll, Joshua A., et al. “Accountable Algorithms.” University of Pennsylvania Law Review 165 (2016). http://bit.ly/2i6ipcO.

  • This paper suggests that policy and legal standards need to be updated given the increased use of algorithms to perform tasks and make decisions in arenas that people once did. An “accountability mechanism” is lacking in many of these automated decision making processes.
  • The paper argues that mere transparency through the divulsion of source code is inadequate when confronting questions of accountability. Rather, technology itself provides a key to create algorithms and decision making apparatuses more inline with our existing political and legal frameworks.
  • The paper assesses some computational techniques that may provide possibilities to create accountable software and reform specific cases of automated decisionmaking. For example, diversity and anti-discrimination orders can be built into technology to ensure fidelity to policy choices.

How Crowdsourcing Can Help Public Transport Innovate Successfully in an Era of Rapid Change


Andrew Nash for the Transportation Research Board: “Crowdsourcing is an organized way of involving people in decision-making and production. Crowdsourced products, like Wikipedia, are replacing established products. Crowdsourced services, like Uber, are replacing established services. Crowdsourced advice, like Trip Advisor, is replacing established experts. Crowdsourcing is becoming ubiquitous as people and organizations realize that it helps them make better decisions and produce better products. This paper is predicated on the belief that crowdsourcing is the key innovation needed for public transport to thrive in this age of rapid change. It presents a model structure for helping understand crowdsourcing and examples of how public transport organizations can use crowdsourcing. The paper presents a broad survey of crowdsourcing with the objective of helping practitioners and researchers implement and understand crowdsourcing projects in public transport…(More)”

Citizen Science and Crowdsourcing for Earth Observations: An Analysis of Stakeholder Opinions on the Present and Future


Suvodeep Mazumdar, Stuart Wrigley and Fabio Ciravegna in Remote Sense: “The impact of Crowdsourcing and citizen science activities on academia, businesses, governance and society has been enormous. This is more prevalent today with citizens and communities collaborating with organizations, businesses and authorities to contribute in a variety of manners, starting from mere data providers to being key stakeholders in various decision-making processes. The “Crowdsourcing for observations from Satellites” project is a recently concluded study supported by demonstration projects funded by European Space Agency (ESA). The objective of the project was to investigate the different facets of how crowdsourcing and citizen science impact upon the validation, use and enhancement of Observations from Satellites (OS) products and services. This paper presents our findings in a stakeholder analysis activity involving participants who are experts in crowdsourcing, citizen science for Earth Observations. The activity identified three critical areas that needs attention by the community as well as provides suggestions to potentially help in addressing some of the challenges identified….(More)”.

The Open Data Movement: Young Activists between Data Disclosure and Digital Reputation


Davide Arcidiacono and Giuseppe Reale in PArtecipazione e COnflitto: “Young citizens show an increasing interest for direct democracy tools and for the building of a new relationship with public administration through the use of digital platforms. The Open Data issue is part of this transformation. The paper analyzes the Open Data issue from the perspective of a spontaneous and informal group of digital activists with the aim of promoting data disclosure. The study is focused mainly on the case of a specific local movement, named Open Data Sicilia (ODS), combining traditional ethnographic observation with an ethnographic approach. The aim of the study is to detect the social profile of the Open Data movement activists, understanding how is it organized their network, what are the common purposes and solidarity models embodied by this type of movement, what are the resources mobilized and their strategies between on-line and off-line. The ODS case appears interesting for its evolution, its strategy and organizational structure: an elitist and technocratic movement that aspires to a broad constituency. It is an expressive or a reformist movement, rather than an anti-system actor, with features that are similar to a lobby. The case study also shows all the typical characteristics of digital activism, with its fluid boundaries between ethical inspiration of civic engagement and individual interests….(More)”

Using GitHub in Government: A Look at a New Collaboration Platform


Justin Longo at the Center for Policy Informatics: “…I became interested in the potential for using GitHub to facilitate collaboration on text documents. This was largely inspired by the 2012 TED Talk by Clay Shirky where he argued that open source programmers could teach us something about how to do open governance:

Somebody put up a tool during the copyright debate last year in the Senate, saying, “It’s strange that Hollywood has more access to Canadian legislators than Canadian citizens do. Why don’t we use GitHub to show them what a citizen-developed bill might look like?” …

For this research, we undertook a census of Canadian government and public servant accounts on GitHub and surveyed those users, supplemented by interviews with key government technology leaders.

This research has now been published in the journal Canadian Public Administration. (If you don’t have access to the full document through the publisher, you can also find it here).

Despite the growing enthusiasm for GitHub (mostly from those familiar with open source software development), and the general rhetoric in favour of collaboration, we suspected that getting GitHub used in public sector organizations for text collaboration might be an uphill battle – not least of which because of the steep learning curve involved in using GitHub, and its inflexibility when being used to edit text.

The history of computer-supported collaborative work platforms is littered with really cool interfaces that failed to appeal to users. The experience to date with GitHub in Canadian governments reflects this, as far as our research shows.

We found few government agencies having an active presence on GitHub compared to social media presence in general. And while federal departments and public servants on GitHub are rare, provincial, territorial, First Nations and local governments are even rarer.

For individual accounts held by public servants, most were found in the federal government at higher rates than those found in broader society (see Mapping Collaborative Software). Within this small community, the distribution of contributions per user follows the classic long-tail distribution with a small number of contributors responsible for most of the work, a larger number of contributors doing very little on average, and many users contributing nothing.

GitHub is still resisted by all but the most technically savvy. With a peculiar terminology and work model that presupposes a familiarity with command line computer operations and the language of software coding, using GitHub presents many barriers to the novice user. But while it is tempting to dismiss GitHub, as it currently exists, as ill-suited as a collaboration tool to support document writing, it holds potential as a useful platform for facilitating collaboration in the public sector.

As an example, to help understand how GitHub might be used within governments for collaboration on text documents, we discuss a briefing note document flow in the paper (see the paper for a description of this lovely graphic).

screen-shot-2017-01-21-at-8-54-24-pm

A few other finding are addressed in the paper, from why public servants may choose not to collaborate even though they believe it’s the right thing to do, to an interesting story about what propelled the use of GitHub in the government of Canada in the first place….(More)”

Empirical data on the privacy paradox


Benjamin Wittes and Emma Kohse at Brookings: “The contemporary debate about the effects of new technology on individual privacy centers on the idea that privacy is an eroding value. The erosion is ongoing and takes place because of the government and big corporations that collect data on us all: In the consumer space, technology and the companies that create it erode privacy, as consumers trade away their solitude either unknowingly or in exchange for convenience and efficiency.

On January 13, we released a Brookings paper that challenges this idea. Entitled, “The Privacy Paradox II: Measuring the Privacy Benefits of Privacy Threats,” we try to measure the extent to which this focus ignores the significant privacy benefits of the technologies that concern privacy advocates. And we conclude that quantifiable effects in consumer behavior strongly support the reality of these benefits.

In 2015, one of us, writing with Jodie Liu, laid out the basic idea last year in a paper published by Brookings called “The Privacy Paradox: the Privacy Benefits of Privacy Threats.” (The title, incidentally, became the name of Lawfare’s privacy-oriented subsidiary page.) Individuals, we argued, might be more concerned with keeping private information from specific people—friends, neighbors, parents, or even store clerks—than from large, remote corporations, and they might actively prefer to give information remote corporations by way of shielding it from those immediately around them. By failing to associate this concern with the concept of privacy, academic and public debates tends to ignore countervailing privacy benefits associated with privacy threats, and thereby keeps score in a way biased toward the threats side of the ledger.To cite a few examples, an individual may choose to use a Kindle e-reader to read Fifty Shades of Grey precisely because she values the privacy benefit of hiding her book choice from the eyes of people on the bus or the store clerk at the book store, rather than for reasons of mere convenience. This privacy benefit, for many consumers, can outweigh the privacy concern presented by Amazon’s data mining. At the very least, the privacy benefits of the Kindle should enter into the discussion.

To cite a few examples, an individual may choose to use a Kindle e-reader to read Fifty Shades of Grey precisely because she values the privacy benefit of hiding her book choice from the eyes of people on the bus or the store clerk at the book store, rather than for reasons of mere convenience. This privacy benefit, for many consumers, can outweigh the privacy concern presented by Amazon’s data mining. At the very least, the privacy benefits of the Kindle should enter into the discussion.

In this paper, we tried to begin the task for measuring the effect and reasoning that supported the thesis in the “Privacy Paradox” using Google Surveys, an online survey tool….(More)”.

Fighting Ebola with information


Larissa Fast and Adele Waugaman at Global Innovation Exchange: What can be learned from the use of data, information, and digital technologies, such as mobile-based systems and internet connectivity, during the Ebola outbreak response in West Africa? What worked, what didn’t, and how can we apply these lessons to improve data and information flows in the future? This report details key findings and recommendations about the collection, management, analysis, and use of paper-based and digital data and information, drawing upon the insights of more than 130 individuals and organizations who worked tirelessly to end the Ebola outbreak in West Africa in 2014 and 2015….(More)”

Governing with Collective Intelligence


Tom Saunders and Geoff Mulgan at Nesta: “This paper provides an introduction to collective intelligence in government. It aims to be useful and relevant to governments of countries at very different levels of development. It highlights the ways in which governments are better understanding the world around them, drawing on ideas and expertise from their citizens, and encouraging greater scrutiny of their actions.

Collective intelligence is a new term to describe something which is in some respects old, but in other respects changing dramatically thanks to advances in digital technologies. It refers to the ability of large groups – a community, region, city or nation – to think and act intelligently in a way that amounts to more than the sum of their parts.

Key findings

Our analysis of government use of collective intelligence initiatives around the world finds that activities fall into four broad categories:

1. Better understanding facts and experiences: using new digital tools to gather data from many more sources.

2. Better development of options and ideas: tapping into the collective brainpower of citizens to come up with better ideas and options for action.

3. Better, more inclusive decision-making: involving citizens in decision making, from policymaking to planning and budgeting.

4. Better oversight of what is done: encouraging broader involvement in the oversight of government activity, from monitoring corruption to scrutinising budgets, helping to increase accountability and transparency….(More)”

Data Collaboratives as a New Frontier of Cross-Sector Partnerships in the Age of Open Data: Taxonomy Development


Paper by Iryna Susha, Marijn Janssen and Stefaan Verhulst: “Data collaboratives present a new form of cross-sector and public-private partnership to leverage (often corporate) data for addressing a societal challenge. They can be seen as the latest attempt to make data accessible to solve public problems. Although an increasing number of initiatives can be found, there is hardly any analysis of these emerging practices. This paper seeks to develop a taxonomy of forms of data collaboratives. The taxonomy consists of six dimensions related to data sharing and eight dimensions related to data use. Our analysis shows that data collaboratives exist in a variety of models. The taxonomy can help organizations to find a suitable form when shaping their efforts to create public value from corporate and other data. The use of data is not only dependent on the organizational arrangement, but also on aspects like the type of policy problem, incentives for use, and the expected outcome of data collaborative….(More)”

Open or Closed? Open Licensing of Real-Time Public Sector Transit Data


Teresa Scassa and Alexandra Diebel in Journal of e-Democracy: “This paper explores how real-time data are made available as “open data” using municipal transit data as a case study. Many transit authorities in North America and elsewhere have installed technology to gather GPS data in real-time from transit vehicles. These data are in high demand in app developer communities because of their use in communicating predicted, rather than scheduled, transit vehicle arrival times. While many municipalities have chosen to treat real-time GPS data as “open data,” the particular nature of real-time GPS data requires a different mode of access for developers than what is needed for static data files. This, in turn, has created a conflict between the “openness” of the underlying data and the sometimes restrictive terms of use which govern access to the real-time data through transit authority Application Program Interfaces (APIs). This paper explores the implications of these terms of use and considers whether real-time data require a separate standard for openness. While the focus is on the transit data context, the lessons from this area will have broader implications, particularly for open real-time data in the emerging smart cities environment….(More)”