Stefaan Verhulst

About: “Connected papers is a unique, visual tool to help researchers and applied scientists find and explore papers relevant to their field of work.

How does it work?

• To create each graph, we analyze an order of ~50,000 papers and select the few dozen with the strongest connections to the origin paper.
• In the graph, papers are arranged according to their similarity. That means that even papers that do not directly cite each other can be strongly connected and very closely positioned. Connected Papers is not a citation tree.
• Our similarity metric is based on the concepts of Co-citation and Bibliographic Coupling. According to this measure, two papers that have highly overlapping citations and references are presumed to have a higher chance of treating a related subject matter.
• Our algorithm then builds a Force Directed Graph to distribute the papers in a way that visually clusters similar papers together and pushes less similar papers away from each other. Upon node selection we highlight the shortest path from each node to the origin paper in similarity space.
• Our database is connected to the Semantic Scholar Paper Corpus (licensed under ODC-BY). Their team has done an amazing job of compiling hundreds of millions of published papers across many scientific fields.…(More)”.

Book by Aileen Nielsen: “Fairness is becoming a paramount consideration for data scientists. Mounting evidence indicates that the widespread deployment of machine learning and AI in business and government is reproducing the same biases we’re trying to fight in the real world. But what does fairness mean when it comes to code? This practical book covers basic concerns related to data security and privacy to help data and AI professionals use code that’s fair and free of bias.

Many realistic best practices are emerging at all steps along the data pipeline today, from data selection and preprocessing to closed model audits. Author Aileen Nielsen guides you through technical, legal, and ethical aspects of making code fair and secure, while highlighting up-to-date academic research and ongoing legal developments related to fairness and algorithms.

• Identify potential bias and discrimination in data science models
• Use preventive measures to minimize bias when developing data modeling pipelines
• Understand what data pipeline components implicate security and privacy concerns
• Write data processing and modeling code that implements best practices for fairness
• Recognize the complex interrelationships between fairness, privacy, and data security created by the use of machine learning models
• Apply normative and legal concepts relevant to evaluating the fairness of machine learning models…(More)”.

Paper by James Pow: “There are two important dimensions to the membership of mini-publics that are distinct from the membership of conventional representative institutions: the selection mechanism (sortition) and the profile of the body’s eligible membership (‘ordinary’ citizens). This article examines the effects of these design features on perceived legitimacy. A survey experiment in the deeply divided context of Northern Ireland finds no evidence that variation in mini-public selection features has an overall effect on perceived legitimacy, but there are important individual-level differences….(More)”.

Peter Addo and Stefaan G. Verhulst in The Conversation: “….This month, the GovLab and the French Development Agency (AFD) released a report looking at precisely these possibilities. “Emerging Uses of Technology for Development: A New Intelligence Paradigm” examines how development practitioners are experimenting with emerging forms of technology to advance development goals. It considers when practitioners might turn to these tools and provides some recommendations to guide their application.

Broadly, the report concludes that experiments with new technologies in development have produced value and offer opportunities for progress. These technologies – which include data intelligence, artificial intelligence, collective intelligence, and embodied intelligence tools – are associated with different prospective benefits and risks. It is essential they be informed by design principles and practical considerations.

Four intelligences

The report derives its conclusions from an analysis of dozens of projects around Africa, including Senegal, Tanzania, Uganda. Linking practice and theory, this approach allows us to construct a conceptual framework that helps development practitioners allocate resources and make key decisions based on their specific circumstances. We call this framework the “four intelligences” paradigm; it offers a way to make sense of how new and emerging technologies intersect with the development field….(More)” (Full Report).

Brief by Rashida Richardson: “Facial-recognition technology is increasingly common throughout society. We can unlock our phones with our faces, smart doorbells let us know who is outside our home, and sentiment analysis allows potential employers to screen interviewees for desirable traits. In the public sector, facial recognition is now in widespread use—in schools, public housing, public transportation, and other areas. Some of the most worrying applications of the technology are in law enforcement, with police departments and other bodies in the United States, Europe, and elsewhere around the world using public and private databases of photos to identify criminal suspects and conduct real-time surveillance of public spaces.

Despite the widespread use of facial recognition and the concerns it presents for privacy and civil liberties, this technology is only subject to a patchwork of laws and regulations. Certain jurisdictions have imposed bans on its use while others have implemented more targeted interventions. In some cases, laws and regulations written to address other technologies may apply to facial recognition as well.

This brief first surveys how facial-recognition technology has been deployed in the public sector around the world. It then reviews the spectrum of proposed and pending laws and regulations that seek to mitigate or address human and civil rights concerns associated with government use of facial recognition, including:

• moratoriums and bans
• standards, limitations, and requirements regarding databases or data sources
• data regulations
• oversight and use requirements
• government commissions, consultations, and studies…(More)”

Sharon Otterman at New York Times: “Huge Ma, a 31-year-old software engineer for Airbnb, was stunned when he tried to make a coronavirus vaccine appointment for his mother in early January and saw that there were dozens of websites to check, each with its own sign-up protocol. The city and state appointment systems were completely distinct.

“There has to be a better way,” he said he remembered thinking.

So, he developed one. In less than two weeks, he launched TurboVax, a free website that compiles availability from the three main city and state New York vaccine systems and sends the information in real time to Twitter. It cost Mr. Ma less than $50 to build, yet it offers an easier way to spot appointments than the city and state’s official systems do.

“It’s sort of become a challenge to myself, to prove what one person with time and a little motivation can do,” he said last week. “This wasn’t a priority for governments, which was unfortunate. But everyone has a role to play in the pandemic, and I’m just doing the very little that I can to make it a little bit easier.”

Supply shortages and problems with access to vaccination appointments have been some of the barriers to the equitable distribution of the vaccine in New York City and across the United States, officials have acknowledged….(More)”.

Article by  Theresa Crimmins, Erin Posthumus, and Kathleen Prudic: “The rapid spread of COVID-19 in 2020 disrupted field research and environmental monitoring efforts worldwide. Travel restrictions and social distancing forced scientists to cancel studies or pause their work for months. These limits measurably reduced the accuracy of weather forecasts and created data gaps on issues ranging from bird migration to civil rights in U.S. public schools.

Our work relies on this kind of information to track seasonal events in nature and understand how climate change is affecting them. We also recruit and train citizens for community science – projects that involve amateur or volunteer scientists in scientific research, also known as citizen science. This often involves collecting observations of phenomena such as plants and animals, daily rainfall totals, water quality or asteroids.

Participation in many community science programs has skyrocketed during COVID-19 lockdowns, with some programs reporting record numbers of contributors. We believe these efforts can help to offset data losses from the shutdown of formal monitoring activities….(More)”.

InterAgency Standing Committee: “Data responsibility in humanitarian action is the safe, ethical and effective management of personal and non-personal data for operational response. It is a critical issue for the humanitarian system to address and the stakes are high. Ensuring we ‘do no harm’ while maximizing the benefits of data requires collective action that extends across all levels of the humanitarian system. Humanitarians must be careful when handling data to avoid placing already vulnerable individuals and communities at further risk. This is especially important in contexts where the urgency of humanitarian needs drives pressure for fast, sometimes untested, data solutions, and the politicization of data can have more extreme consequences for people. 

The implementation of data responsibility in practice is often inconsistent within and across humanitarian response contexts. This is true despite established principles, norms and professional standards regarding respect for the rights of affected populations; the range of resources on data responsibility available in the wider international data community; as well as significant efforts by many humanitarian organizations to develop and update their policies and guidance in this area. However, given that the humanitarian data ecosystem is inherently interconnected, no individual organization can tackle all these challenges alone. 

This system-wide Operational Guidance, which is a first, will ensure concrete steps for data responsibility in all phases of humanitarian action. It is the result of an inclusive and consultative process, involving more than 250 stakeholders from the humanitarian sector. Partners across the system will implement these guidelines in accordance with their respective mandates and the decisions of their governing bodies….(More)”

Paper by Sophie Stalla-Bourdillon, Laura Carmichael and Alexsis Wintour: “Independent data stewardship remains a core component of good data governance practice. Yet, there is a need for more robust independent data stewardship models that are able to oversee data-driven, multi-party data sharing, usage and re-usage, which can better incorporate citizen representation, especially in relation to personal data. We propose that data foundations—inspired by Channel Islands’ foundations laws—provide a workable model for good data governance not only in the Channel Islands, but also elsewhere. A key advantage of this model—in addition to leveraging existing legislation and building on established precedent—is the statutory role of the guardian that is a unique requirement in the Channel Islands, and when interpreted in a data governance model provides the independent data steward. The principal purpose for this paper, therefore, is to demonstrate why data foundations are well suited to the needs of data sharing initiatives. We further examine how data foundations could be established in practice—and provide key design principles that should be used to guide the design and development of any data foundation….(More)”.

Open Access Book by Ken Steif: “… teaches readers how to address complex public policy problems with data and analytics using reproducible methods in R. Each of the eight chapters provides a detailed case study, showing readers: how to develop exploratory indicators; understand ‘spatial process’ and develop spatial analytics; how to develop ‘useful’ predictive analytics; how to convey these outputs to non-technical decision-makers through the medium of data visualization; and why, ultimately, data science and ‘Planning’ are one and the same. A graduate-level introduction to data science, this book will appeal to researchers and data scientists at the intersection of data analytics and public policy, as well as readers who wish to understand how algorithms will affect the future of government….(More)”.