Anni Rowland-Campbell and Wendy Hall
Almost a decade ago a small group of people from industry and government, organised a series of conferences in Canberra which sought to bring various communities together to talk about the emerging issues related to humans and technologies (see metalounge).
At the same time a group of luminaries from the Web world were creating Web Science and in the intervening period Web Science has gone on to contribute greatly to the global conversation around the evolving Social Machine (see 10th anniversary video below).
Brave Conversations gives people the space to imagine alternative futures through robust debate and respectful curiosity, learning from each other, playing with ideas, and asking the questions that are both confronting and potentially will take us to uncomfortable places.
This is not an academic workshop, and there will be no formal published presentations or papers. Our aim is to draw together people from academia, government, business, the media and the third sector - to address some of the profound issues that are arising as human life becomes progressively entwined with the internet and the Web.
We want to encourage debate, critical thinking, creative design and social awareness. We want to push the boundaries in terms of thinking about the World and the Web. Our focus is on helping to develop “smart humans” for the digital age.
Linked Learning 2019: 8th International Workshop on Learning and Education with Web Data (#LILE2019)
Ran Yu, Mathieu D'Aquin, Dragan Gasevic, Joachim Kimmerle, Eelco Herder and Ralph Ewerth
Building on the previous seven editions and its growing community, LILE2019 will provide an interdisciplinary forum for researchers and practitioners who make innovative use of Web data for educational purposes, spanning areas such as learning analytics, Web mining, data and Web science, psychology and the social sciences. The previous editions of the LILE workshop were successfully held at the ESWC, WWW, ISWC and WebSci conferences. LILE2019 targets a public full day workshop that consists of keynotes, presentations of accepted papers and posters and discussion. The intended audience consists of researchers and practitioners from the general areas of web science, learning analytics, psychology, computational social science or the social sciences.
Data Sharing – Trust, Collaboratives, Co-Ops - What are they and why does it matter?
Johanna Walker, Elena Simperl, Gefion Thuermer and Dumitru Roman
Open Data has been a goal pursued primarily by governments, to unlock previously inaccessible value and accelerate growth of digital businesses. This has been tremendously successful in the US as well as across Europe. While open data is open to all, there is also benefit in sharing data that is not public. Existing examples of this include data marketplaces such as Dawex, or funding programmes like the European Data Incubator or Data Pitch where shared data creates innovation. However, the process of sharing closed data frequently involves complex negotiations and legal agreements. With the introduction of the European General Data Protection regulation (GDPR) in May 2018, considerations of privacy and data protection have become more important, as data subjects gained more explicit, and enforceable rights, while sharing data across borders became easier. Businesses in Europe are already embracing data trusts such as Trūata to ensure compliance, but more institutions are required to address the complications of these processes, and consolidate the interests of data subjects, processors and owners.
This consolidation could happen through the – still conceptual – frameworks of Data Trusts, Data Collaboratives or Data Cooperatives. These institutions are intended to simplify the use of data by enabling an environment in which is can be shared while maintaining the trust of all parties involved. While Data Collaboratives are an idea born in the US-based GovLab (Verhulst & Sangokoya, 2015), Data Trusts have been brought into focus in the UK by an Independent Review on AI (Hall & Pesenti, 2017). Their goals are broadly the same: Institutionalised oversight of the handling and sharing of different types of data, such as government data, but also corporate, industrial, research, or aggregate personal data, which is not intended for the public domain (McDonald, 2018; Hardinges, 2018, Verhulst, 2018).
Due to their potentially central role in data sharing environments, these institutions are also of particular interest for academics from a broad variety of fields, including Computer Science, Law, Political Science, and Sociology. There is very little published research treating them as an object of study in their own right and placing them in a broad socio-technological context. This workshop aims to investigate the potential for coherent multidisciplinary research into the institutional frameworks that enable the sharing of data, what their development means for society as a whole, why this matters, and how they might work.
Handling Web Bias
Ricardo Baeza-Yates and Jeanna Matthews
A key aspect of the Web Science conference is exploring the ethical challenges of technologies, data, algorithms, platforms, and people in the web as well as detecting, preventing and predicting anomalies in web data including algorithmic and data biases. We propose a workshop to focus on best practices for identifying and handling web bias. Awareness of the problems of algorithmic and data bias have been growing but even with careful review of the algorithms and data sets, it may not be possible to delete all unwanted bias, particularly when systems learn from historical data that encodes historical biases.
Having a dedicated workshop will help, we believe, to take a rich and cross-domain approach to this complicated problem. The objective of this workshop is to provide a venue for researchers to move beyond awareness of the problem of algorithmic and data bias to focus on practical strategies for handling it. We welcome case studies that illustrate strategies that have worked and strategies that have not worked in practice.
In recent years, a collection of workshops and conferences have been founded to explore issues of
fairness, accountability, transparency and ethics as they intersect with research in a diverse set of
technical areas (e.g. FAT-ML to explore issues of fairness, accountability and transparency in machine learning, FAT-IR to explore these issues in information retrieval and others). Many of these emerging venues have been highly multi-disciplinary bringing together researchers from computing, law, policy, ethics, anthropology and art. Given the fundamentally multi-disciplinary nature of the Web Science conference, we expect to find a workshop focused on these issues at Web Science to find an especially vibrant home.
Online Interactive Experiments on Networks
Conducting human experiments using crowdsourcing platforms, such as Amazon Mechanical Turk, has made it possible to collect a much larger amount of experimental data in a much shorter period of time relative to what was possible in traditional physical lab settings. This has provided a new suite of methods for conducting randomized experiments in socio-technical systems, allowing for straightforward causal inference. However, using crowdsourcing platforms to experimentally study real-time interactions between individuals presents numerous practical challenges. These studies need fairly large groups of subjects to be present simultaneously in each session, and outcomes typically occur at the level of the group (i.e., session) rather than the individual. Yet most crowdsourcing platforms are not designed to facilitate simultaneous structured interactions between subjects. Thus, it can difficult (and expensive) to recruit enough participants to achieve a sufficient degree of statistical power (especially for session-level outcomes). In this tutorial, we will discuss best practices for designing and conducting online social network experiments where human subjects (and programmed bots) interact simultaneously within a specified network structure. We will show how the experimental design can be informed by computational models in an iterative process (i.e., using experimental data to calibrate the computational model and use the computational model to optimize the design and find the right parameters for the experiments). We will also introduce additional tools/platforms that facilitate conducting such studies and walk the audience through the implementation steps of a typical experiment on networks using customized and publicly available software.
Build your own Online Lab with Volunteer Science
Jason Radford and David Lazer
New information technologies allow for new modes of data collection and provide unparalleled access to people across the globe. Human beings are producing more and more digital data about themselves that can be used for research. Programs like Galaxy Zoo, Fold.It, and ReCAPTCHA demonstrate the power of recruiting people online to participate in and contribute to research. However these projects require extensive technical expertise and funding to create and maintain. Volunteer Science is a platform built to help researchers create their own online studies and recruit participants of all kinds. In this tutorial, we will teach participants how to use Volunteer Science to build their own online research using pre-built study templates or their own custom studies. We will also teach attendees the best ways to recruit participants for different studies, including volunteers, paid workers, and groups of participants. Participants will come away ready to conduct online studies and recruit their own panel of participants.
Web research ethics: Confidentiality, consent, data integrity & more
Katharina Kinder-Kurlanda and Michael Zimmer
As researchers who are studying the web we find ourselves immersed in a domain where information flows freely but is also potentially bound by contextual norms and expectations, where platforms may oscillate between open and closed information, and where data may be user-generated, filtered, algorithmically-processed, or proprietary. When using the internet as a tool or a space of research, web scientists are thus confronted with a continuously expanding set of ethical dilemmas: What ethical obligations do we have to protect the privacy of subjects engaging in activities in “public” internet spaces? How is and should informed consent be obtained online? How should we conduct research on vulnerable groups, criminal or terrorist organizations or hate speech? How should we contend with inequalities in data access and uncertainties about data provenance and quality? In the tutorial we will actively engage with concrete example cases of common, not so common, tricky, interesting and puzzling ethical dilemmas. Some in-depth ethical thinking and theory, as well as very practical and creative solutions, will be explored. Participants will also have the chance to bring their own questions or ethical dilemmas to the workshop (it will be possible to ‘submit’ cases in advance to be discussed in an ethics ‘clinic’) for discussion and help to find solutions.
Re-decentralizing the Web with Solid
Tim Berners-Lee and Ruben Verborgh
The Semantic Web is increasingly becoming a centralized story: we rely on large-scale server-side infrastructures to perform intense reasoning, data mining, and query execution. This kind of centralization leads to a number of problems, including lock-in effects, lack of users' control of their data, limited incentives for interoperability and openness, and the resulting detrimental effects on privacy and innovation. Therefore, we urgently need research and engineering to bring back the “Web” to the Semantic Web, aiming for intelligent clients—instead of intelligent servers—as sketched in the original Semantic Web vision. Solid empowers users and organizations to separate their data from the applications that use it. It allows people to look at the same data with different apps at the same time. It opens brand new avenues for creativity, problem-solving, and commerce. This tutorial will give participants an overview of Solid and hands-on experience to start building with Solid.
February 8, 2019
Proposal submission deadline
February 15, 2019
March 1, 2019
Websites and CfPs of workshops/tutorials online
June 30, 2019
Workshop & tutorial date