AI is susceptible to misuse and has been found to reflect biases that exist in society. Fortunately, there are a number of organizations committed to addressing ethical questions in AI. We list our top 10.
In The Code: Silicon Valley and the Remaking of America, Margaret O’Mara provides a new account of the region’s evolution that brings the US government into the story. The book offers a compelling narrative that tracks the key players and events that have underpinned Silicon Valley’s tremendous, but messy, rise, writes Robyn Klingler-Vidra, while also underscoring the gender imbalance and casual misogyny that has been a longstanding characteristic of its culture.
Coming from a social science background, I have had very limited exposure to data science. I was therefore excited to learn about the emerging field of computational social science and the Summer Institute in Computational Social Science (SICSS) presented the right opportunity. I applied to the 2019 SICSS and I was accepted for the Cape Town partner site. I went in not knowing what to expect but by the end of the first day I knew the experience at the two-week Summer Institute was going to be truly worthwhile.
This summer we've had the pleasure of welcoming four Masters students from UK universities to work with the SAGE Ocean team. All four students have been quite incredible, and have managed to produce a variety of outputs and substantially contribute to our work. In this blog post, they share testimonials of their time in the team.
Whether you call it ‘content analysis’, ‘textual data labeling’, ‘hand-coding’, or ‘tagging’, a lot more researchers and data science teams are starting up annotation projects these days. Many want human judgment labeled onto text to train AI (via supervised machine learning approaches). Others have tried automated text analysis and found it wanting. Now they’re looking for ways to label text that aren’t so hard to interpret and explain.
In April this year a special collection examining social media and politics was published in SAGE Open. Guest edited by Joshua A. Tucker and Pablo Barberá, the articles grew out of a series of conferences held by NYU’s Social Media and Political Participation lab (SMaPP) and the NYU Global Institute for Advanced Study (GIAS) known as SMaPP-Global. Upon publication Joshua Tucker said ‘the collection of articles also shows the value of exposing researchers from a variety of disciplines with similar substantive interests to each other's work at regular intervals’. Interdisciplinary collaborative research projects are a cornerstone of what makes computational social science such an interesting field. We were intrigued to know more so caught up with Josh and Pablo to hear more.
The baton was passed to the University of Amsterdam for the fifth addition of IC2S2 with the core conference taking place between 18-20 July.
Text is everywhere, and everything is text. More textual data than ever before are available to computational social scientists—be it in the form of digitized books, communication traces on social media platforms, or digital scientific articles. Researchers in academia and industry increasingly use text data to understand human behavior and to measure patterns in language. Techniques from natural language processing have created a fertile soil to perform these tasks and to make inferences based on text data on a large scale.
Academics face various pressures, from research teaching and administrative duties. The best way to create a positive culture in academia is to share. However, it may sometimes feel like there is no incentive to share teaching materials, if I have spent so many hours developing this work, why should I just hand it over to someone, “what’s in it for me?”
For centuries, being a scientist has meant learning to live with limited data. People only share so much on a survey form. Experiments don’t account for all the conditions of real world situations. Field research and interviews can only be generalized so far. Network analyses don’t tell us everything we want to know about the ties among people. And text/content/document analysis methods allow us to dive deep into a small set of documents, or they give us a shallow understanding of a larger archive. Never both. So far, the truly great scientists have had to apply many of these approaches to help us better see the world through their kaleidoscope of imperfect lenses.
It’s all about incentives. The current academic ecosystem incentivises publication in high impact factor journals and grant capture above all else, but there is more to being an academic than producing journal articles and winning grants. Luckily there are an increasing number of initiatives that are helping academics get credit for more of the work they do and increase their broader impact. This post rounds up some of the most interesting efforts.
Here at SAGE Ocean, we’ve been collecting data on the landscape of tools for computational social science. While looking through the data, we found an incredible variety, from resources to aid crowdsourcing to text analysis to social media analysis. Despite this diversity at the technical level of the tools, we found a persistent lack of diversity in terms of who built these tools.
Today, researchers are using LinkedIn data in a variety of ways: to find and recruit participants for research and experiments (Using Facebook and LinkedIn to Recruit Nurses for an Online Survey), to analyze how the features of this network affect people’s behavior and identity or how data is used for hiring and recruiting purposes, or most often to enrich other data sources with publicly available information from selected LinkedIn profiles (Examining the Career Trajectories of Nonprofit Executive Leaders, The Tech Industry Meets Presidential Politics: Explaining the Democratic Party’s Technological Advantage in Electoral Campaigning).
At CogX, the Festival of AI and Emergent Technology, two icons appeared over and over across the King’s Cross location. The first was the logo for the festival itself, an icon of a brain with lobes made up of wires. The second was for the 2030 UN Sustainable Development Goals (SDGs), a partner of the festival. The SDG icon is a circle split into 17 differently colored segments, each representing one of the goals for 2030—aims like zero hunger and no poverty. The idea behind this partnership was to encourage participants of CogX—speakers, presenters, expo attendees—to think about how their products and innovations could be used to help achieve these SDGs.
Following the launch of the SAGE Ocean initiative in February 2018, the inaugural winners of the SAGE Concept Grant program were announced in March of the same year. As we build up to this year’s winner announcement we’ve caught up with the three winners from 2018 to see what they’ve been up to and how the seed funding has helped in the development of their tools.
In this post we chatted to MiniVan, a project of the Public Data Lab.
The beginning of term is nearing. You’re teaching a new module on Computational Social Science (CSS). The field is developing rapidly and so are best practices around teaching the theory, methods and techniques to students.
Where do you start when you’re putting together your teaching materials? Do you visit the websites and blogs of academics who are experienced in teaching CSS to look for resources? Do you search online for syllabi, reading lists and tutorials? Maybe you scour YouTube for videos to include in your slides?
Together with a group of UK academics, the SAGE Ocean team have been digging into where academics go to find teaching materials and what the barriers are for academics who want to share, reuse and give and get credit for the materials they produce for teaching. This post includes thoughts from the group on what’s needed to promote a stronger culture of sharing teaching materials in CSS. And we’ve curated a list of our favorite resources for you too!
As the participants gear up for the 2019 Summer Institute in Computational Social Science (SICSS), starting June 16th at Princeton and the 11 alumni-led partner locations situated right across the globe, we caught up with the founders of the SICSS, Chris Bail and Matt Salganik to find out how it all got going, the move to a data intensive society and the benefits of learning data science skills to make the most of this new data.
Sam Gilbert demonstrates the value of big search data for social scientists, and suggests some practical steps to using internet search data in your own research.
Social media has brought about rapid change in society, from our social interactions and complaint systems to our elections and media outlets. It is increasingly used by individuals and organizations in both the public and private sectors. Over 30% of the world’s population is on social media. We spend most of our waking hours attached to our devices, with every minute in the US, 2.1M snaps are created and 1M people are logging in to Facebook. With all this use, comes a great amount of data.
SAGE Campus are pleased to announce that we are launching two new courses to our suite of online data science courses for social scientists. The new short courses, Research Design in Social Data Science and Collecting Social Media Data, are aimed at those studying, teaching or working in social science disciplines who are looking to take their first steps toward working with big-data driven approaches to social science research.