research paper / thesis / dissertation

Open Data: A 21st Century Asset for Small and Medium Sized Enterprises

Curated on August 20, 2015August 3, 2018 by Stefaan Verhulst

“The economic and social potential of open data is widely acknowledged. In particular, the business opportunities have received much attention. But for all the excitement, we still know very little about how and under what conditions open data really works.

To broaden our understanding of the use and impact of open data, the GovLab has a variety of initiatives and studies underway. Today, we share publicly our findings on how Small and Medium Sized Enterprises (SMEs) are leveraging open data for a variety of purposes. Our paper “Open Data: A 21^st Century Asset for Small and Medium Sized Enterprises” seeks to build a portrait of the lifecycle of open data—how it is collected, stored and used. It outlines some of the most important parameters of an open data business model for SMEs….

The paper analyzes ten aspects of open data and establishes ten principles for its effective use by SMEs. Taken together, these offer a roadmap for any SME considering greater use or adoption of open data in its business.

Among the key findings included in the paper:

SMEs, which often lack access to data or sophisticated analytical tools to process large datasets, are likely to be one of the chief beneficiaries of open data.

Government data is the main category of open data being used by SMEs. A number of SMEs are also using open scientific and shared corporate data.

Open data is used primarily to serve the Business-to-Business (B2B) markets, followed by the Business-to-Consumer (B2C) markets. A number of the companies studied serve two or three market segments simultaneously.

Open data is usually a free resource, but SMEs are monetizing their open-data-driven services to build viable businesses. The most common revenue models include subscription-based services, advertising, fees for products and services, freemium models, licensing fees, lead generation and philanthropic grants.

The most significant challenges SMEs face in using open data include those concerning data quality and consistency, insufficient financial and human resources, and issues surrounding privacy.

This is just a sampling of findings and observations. The paper includes a number of additional observations concerning business and revenue models, product development, customer acquisition, and other subjects of relevance to any company considering an open data strategy.”

Content Volatility of Scientific Topics in Wikipedia: A Cautionary Tale

Curated on August 19, 2015October 23, 2018 by Stefaan Verhulst

Paper by Wilson AM and Likens GE at PLOS: “Wikipedia has quickly become one of the most frequently accessed encyclopedic references, despite the ease with which content can be changed and the potential for ‘edit wars’ surrounding controversial topics. Little is known about how this potential for controversy affects the accuracy and stability of information on scientific topics, especially those with associated political controversy. Here we present an analysis of the Wikipedia edit histories for seven scientific articles and show that topics we consider politically but not scientifically “controversial” (such as evolution and global warming) experience more frequent edits with more words changed per day than pages we consider “noncontroversial” (such as the standard model in physics or heliocentrism). For example, over the period we analyzed, the global warming page was edited on average (geometric mean ±SD) 1.9±2.7 times resulting in 110.9±10.3 words changed per day, while the standard model in physics was only edited 0.2±1.4 times resulting in 9.4±5.0 words changed per day. The high rate of change observed in these pages makes it difficult for experts to monitor accuracy and contribute time-consuming corrections, to the possible detriment of scientific accuracy. As our society turns to Wikipedia as a primary source of scientific information, it is vital we read it critically and with the understanding that the content is dynamic and vulnerable to vandalism and other shenanigans….(More)”

e-Consultation Platforms: Generating or Just Recycling Ideas?

Curated on August 18, 2015August 3, 2018 by Stefaan Verhulst

Chapter by Efthimios Tambouris, Anastasia Migotzidou, and Konstantinos Tarabanis in Electronic Participation: “A number of governments worldwide employ web-based e-consultation platforms to enable stakeholders commenting on draft legislation. Stakeholders’ input includes arguing in favour or against the proposed legislation as well as proposing alternative ideas. In this paper, we empirically investigate the relationship between the volume of contributions in these platforms and the amount of new ideas that are generated. This enables us to determine whether participants in such platforms keep generating new ideas or just recycle a finite number of ideas. We capitalised on argumentation models to code and analyse a large number of draft law consultations published inopengov.gr, the official e-consultation platform for draft legislation in Greece. Our results suggest that as the number of posts grows, the number of new ideas continues to increase. The results of this study improve our understanding of the dynamics of these consultations and enable us to design better platforms….(More)”

One way traffic: The open data initiative project and the need for an effective demand side initiative in Ghana

Curated on August 15, 2015August 3, 2018 by Stefaan Verhulst

Paper by Frank L. K. Ohemeng and Kwaku Ofosu-Adarkwa in the Government Information Quarterly: “In recent years the necessity for governments to develop new public values of openness and transparency, and thereby increase their citizenries’ sense of inclusiveness, and their trust in and confidence about their governments, has risen to the point of urgency. The decline of trust in governments, especially in developing countries, has been unprecedented and continuous. A new paradigm that signifies a shift to citizen-driven initiatives over and above state- and market-centric ones calls for innovative thinking that requires openness in government. The need for this new synergy notwithstanding, Open Government cannot be considered truly open unless it also enhances citizen participation and engagement. The Ghana Open Data Initiative (GODI) project strives to create an open data community that will enable government (supply side) and civil society in general (demand side) to exchange data and information. We argue that the GODI is too narrowly focused on the supply side of the project, and suggest that it should generate an even platform to improve interaction between government and citizens to ensure a balance in knowledge sharing with and among all constituencies….(More)”

What factors influence transparency in US local government?

Curated on August 15, 2015August 3, 2018 by Stefaan Verhulst

Grichawat Lowatcharin and Charles Menifield at LSE Impact Blog: “The Internet has opened a new arena for interaction between governments and citizens, as it not only provides more efficient and cooperative ways of interacting, but also more efficient service delivery, and more efficient transaction activities. …But to what extent does increased Internet access lead to higher levels of government transparency? …While we found Internet access to be a significant predictor of Internet-enabled transparency in our simplest model, this finding did not hold true in our most extensive model. This does not negate that fact that the variable is an important factor in assessing transparency levels and Internet access. …. Our data shows that total land area, population density, percentage of minority, education attainment, and the council-manager form of government are statistically significant predictors of Internet-enabled transparency. These findings both confirm and negate the findings of previous researchers. For example, while the effect of education on transparency appears to be the most consistent finding in previous research, we also noted that the rural/urban (population density) dichotomy and the education variable are important factors in assessing transparency levels. Hence, as governments create strategic plans that include growth models, they should not only consider the budgetary ramifications of growth, but also the fact that educated residents want more web based interaction with government. This finding was reinforced by a recent Census Bureau report indicating that some of the cities and counties in Florida and California had population increases greater than ten thousand persons per month during the period 2013-2014.

This article is based on the paper ‘Determinants of Internet-enabled Transparency at the Local Level: A Study of Midwestern County Web Sites’, in State and Local Government Review. (More)”

New Privacy Research Has Implications for Design and Policy

Curated on August 14, 2015August 3, 2018 by Stefaan Verhulst

Jedidiah Bracy at PrivacyTech: “Try visualizing the Internet’s basic architecture. Could you draw it? What would be your mental model for it?

Let’s be more specific: Say you just purchased shoes off a website using your mobile phone at work. How would you visualize that digital process? Would a deeper knowledge of this architecture make more apparent the myriad potential privacy risks in this transaction? Or to put it another way, what would your knowledge, or lack thereof, for these architectural underpinnings reveal about your understanding of privacy and security risks?

Whether you’re a Luddite or a tech wiz, creating these mental models of the Internet is not the easiest endeavor. Just try doing so yourself.

It is an exercise, however, that several individuals underwent for new research that has instructive implications for privacy and security pros.

“So everything I do on the Internet or that other people do on the Internet is basically asking the Internet for information, and the Internet is sending us to various places where the information is and then bringing us back.” – CO1

You’d think those who have a better understanding of how the Internet works would probably have a better understanding of the privacy and security risks, right? Most likely. Paradoxically, though, a better technological understanding may have very little influence on an individual’s response to potential privacy risks.

This is what a dedicated team of researchers from Carnegie Mellon University worked to discover recently in their award-winning paper, “My Data Just Goes Everywhere”: User Mental Models of the Internet and Implications for Privacy and Security—a culmination of research from Ruogu Kang, Laura Dabbish, Nathaniel Fruchter and Sara Kiesler—all from CMU’s Human-Computer Interaction Institute and the Heinz College in Pittsburgh, PA.

“I try to browse through the terms and conditions but there’s so much there I really don’t retain it.” – T11

Presented at the CyLab Usable Privacy and Security Laboratory’s (CUPS) 11^thSymposium on Usable Privacy and Security (SOUPS), their research demonstrated that even though savvy and non-savvy users of the Internet have much different perceptions of its architecture, such knowledge was not predictive of whether a user would take the necessary steps to protect their privacy online. Experience, rather, appears to play a more determinate role.

Kang, who led the team, said she was surprised by the results….(More)”

Mining Administrative Data to Spur Urban Revitalization

Curated on August 14, 2015August 3, 2018 by Stefaan Verhulst

New paper by Ben Green presented at the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining: “After decades of urban investment dominated by sprawl and outward growth, municipal governments in the United States are responsible for the upkeep of urban neighborhoods that have not received sufficient resources or maintenance in many years. One of city governments’ biggest challenges is to revitalize decaying neighborhoods given only limited resources. In this paper, we apply data science techniques to administrative data to help the City of Memphis, Tennessee improve distressed neighborhoods. We develop new methods to efficiently identify homes in need of rehabilitation and to predict the impacts of potential investments on neighborhoods. Our analyses allow Memphis to design neighborhood-improvement strategies that generate greater impacts on communities. Since our work uses data that most US cities already collect, our models and methods are highly portable and inexpensive to implement. We also discuss the challenges we encountered while analyzing government data and deploying our tools, and highlight important steps to improve future data-driven efforts in urban policy….(More)”

Push, Pull, and Spill: A Transdisciplinary Case Study in Municipal Open Government

Curated on August 13, 2015August 3, 2018 by Stefaan Verhulst

New paper by Jan Whittington et al: “Cities hold considerable information, including details about the daily lives of residents and employees, maps of critical infrastructure, and records of the officials’ internal deliberations. Cities are beginning to realize that this data has economic and other value: If done wisely, the responsible release of city information can also release greater efficiency and innovation in the public and private sector. New services are cropping up that leverage open city data to great effect.

Meanwhile, activist groups and individual residents are placing increasing pressure on state and local government to be more transparent and accountable, even as others sound an alarm over the privacy issues that inevitably attend greater data promiscuity. This takes the form of political pressure to release more information, as well as increased requests for information under the many public records acts across the country.

The result of these forces is that cities are beginning to open their data as never before. It turns out there is surprisingly little research to date into the important and growing area of municipal open data. This article is among the first sustained, cross-disciplinary assessments of an open municipal government system. We are a team of researchers in law, computer science, information science, and urban studies. We have worked hand-in-hand with the City of Seattle, Washington for the better part of a year to understand its current procedures from each disciplinary perspective. Based on this empirical work, we generate a set of recommendations to help the city manage risk latent in opening its data….(More)”

Algorithms and Bias

Curated on August 12, 2015August 15, 2018 by Stefaan Verhulst

Q. and A. With Cynthia Dwork in the New York Times: “Algorithms have become one of the most powerful arbiters in our lives. They make decisions about the news we read, the jobs we get, the people we meet, the schools we attend and the ads we see.

Yet there is growing evidence that algorithms and other types of software can discriminate. The people who write them incorporate their biases, and algorithms often learn from human behavior, so they reflect the biases we hold. For instance, research has shown that ad-targeting algorithms have shown ads for high-paying jobs to men but not women, and ads for high-interest loans to people in low-income neighborhoods.

Cynthia Dwork, a computer scientist at Microsoft Research in Silicon Valley, is one of the leading thinkers on these issues. In an Upshot interview, which has been edited, she discussed how algorithms learn to discriminate, who’s responsible when they do, and the trade-offs between fairness and privacy.

Q: Some people have argued that algorithms eliminate discriminationbecause they make decisions based on data, free of human bias. Others say algorithms reflect and perpetuate human biases. What do you think?

A: Algorithms do not automatically eliminate bias. Suppose a university, with admission and rejection records dating back for decades and faced with growing numbers of applicants, decides to use a machine learning algorithm that, using the historical records, identifies candidates who are more likely to be admitted. Historical biases in the training data will be learned by the algorithm, and past discrimination will lead to future discrimination.

Q: Are there examples of that happening?

A: A famous example of a system that has wrestled with bias is the resident matching program that matches graduating medical students with residency programs at hospitals. The matching could be slanted to maximize the happiness of the residency programs, or to maximize the happiness of the medical students. Prior to 1997, the match was mostly about the happiness of the programs.

This changed in 1997 in response to “a crisis of confidence concerning whether the matching algorithm was unreasonably favorable to employers at the expense of applicants, and whether applicants could ‘game the system,’ ” according to a paper by Alvin Roth and Elliott Peranson published in The American Economic Review.

Q: You have studied both privacy and algorithm design, and co-wrote a paper, “Fairness Through Awareness,” that came to some surprising conclusions about discriminatory algorithms and people’s privacy. Could you summarize those?

A: “Fairness Through Awareness” makes the observation that sometimes, in order to be fair, it is important to make use of sensitive information while carrying out the classification task. This may be a little counterintuitive: The instinct might be to hide information that could be the basis of discrimination….

Q: The law protects certain groups from discrimination. Is it possible to teach an algorithm to do the same?

A: This is a relatively new problem area in computer science, and there are grounds for optimism — for example, resources from the Fairness, Accountability and Transparency in Machine Learning workshop, which considers the role that machines play in consequential decisions in areas like employment, health care and policing. This is an exciting and valuable area for research. …(More)”