Metrics at Work: Journalism and the Contested Meaning of Algorithms

Curated on Posted on by Stefaan Verhulst

Book by Angèle Christin: “When the news moved online, journalists suddenly learned what their audiences actually liked, through algorithmic technologies that scrutinize web traffic and activity. Has this advent of audience metrics changed journalists’ work practices and professional identities? In Metrics at Work, Angèle Christin documents the ways that journalists grapple with audience data in the form of clicks, and analyzes how new forms of clickbait journalism travel across national borders.

Drawing on four years of fieldwork in web newsrooms in the United States and France, including more than one hundred interviews with journalists, Christin reveals many similarities among the media groups examined—their editorial goals, technological tools, and even office furniture. Yet she uncovers crucial and paradoxical differences in how American and French journalists understand audience analytics and how these affect the news produced in each country. American journalists routinely disregard traffic numbers and primarily rely on the opinion of their peers to define journalistic quality. Meanwhile, French journalists fixate on internet traffic and view these numbers as a sign of their resonance in the public sphere. Christin offers cultural and historical explanations for these disparities, arguing that distinct journalistic traditions structure how journalists make sense of digital measurements in the two countries.

Contrary to the popular belief that analytics and algorithms are globally homogenizing forces, Metrics at Work shows that computational technologies can have surprisingly divergent ramifications for work and organizations worldwide….(More)”.

An Open-Source Tool to Accelerate Scientific Knowledge Discovery

Curated on Posted on by Stefaan Verhulst

Mozilla: “Timely and open access to novel outputs is key to scientific research. It allows scientists to reproduce, test, and build on one another’s work — and ultimately unlock progress.

The most recent example of this is the research into COVID-19. Much of the work was published in open access journals, swiftly reviewed and ultimately improving our understanding of how to slow the spread and treat the disease. Although this rapid increase in scientific publications is evident in other domains too, we might not be reaping the benefits. The tools to parse and combine this newly created knowledge have roughly remained the same for years.

Today, Mozilla Fellow Kostas Stathoulopoulos is launching Orion — an open-source tool to illuminate the science behind the science and accelerate knowledge discovery in the life sciences. Orion enables users to monitor progress in science, visually explore the scientific landscape, and search for relevant publications.

Orion

Orion collects, enriches and analyses scientific publications in the life sciences from Microsoft Academic Graph.

Users can leverage Orion’s views to interact with the data. The Exploration view shows all of the academic publications in a three-dimensional visualization. Every particle is a paper and the distance between them signifies their semantic similarity; the closer two particles are, the more semantically similar. The Metrics view visualizes indicators of scientific progress and how they have changed over time for countries and thematic topics. The Search view enables the users to search for publications by submitting either a keyword or a longer query, for example, a sentence or a paragraph of a blog they read online….(More)”.

Why Modeling the Spread of COVID-19 Is So Damn Hard


Curated on Posted on by Stefaan Verhulst

Matthew Hutson at IEEE Spectrum: “…Researchers say they’ve learned a lot of lessons modeling this pandemic, lessons that will carry over to the next.

The first set of lessons is all about data. Garbage in, garbage out, they say. Jarad Niemi, an associate professor of statistics at Iowa State University who helps run the forecast hub used by the CDC, says it’s not clear what we should be predicting. Infections, deaths, and hospitalization numbers each have problems, which affect their usefulness not only as inputs for the model but also as outputs. It’s hard to know the true number of infections when not everyone is tested. Deaths are easier to count, but they lag weeks behind infections. Hospitalization numbers have immense practical importance for planning, but not all hospitals release those figures. How useful is it to predict those numbers if you never have the true numbers for comparison? What we need, he said, is systematized random testing of the population, to provide clear statistics of both the number of people currently infected and the number of people who have antibodies against the virus, indicating recovery. Prakash, of Georgia Tech, says governments should collect and release data quickly in centralized locations. He also advocates for central repositories of policy decisions, so modelers can quickly see which areas are implementing which distancing measures.

Researchers also talked about the need for a diversity of models. At the most basic level, averaging an ensemble of forecasts improves reliability. More important, each type of model has its own uses—and pitfalls. An SEIR model is a relatively simple tool for making long-term forecasts, but the devil is in the details of its parameters: How do you set those to match real-world conditions now and into the future? Get them wrong and the model can head off into fantasyland. Data-driven models can make accurate short-term forecasts, and machine learning may be good for predicting complicated factors. But will the inscrutable computations of, for instance, a neural network remain reliable when conditions change? Agent-based models look ideal for simulating possible interventions to guide policy, but they’re a lot of work to build and tricky to calibrate.

Finally, researchers emphasize the need for agility. Niemi of Iowa State says software packages have made it easier to build models quickly, and the code-sharing site GitHub lets people share and compare their models. COVID-19 is giving modelers a chance to try out all their newest tools, says Meyers, of the University of Texas. “The pace of innovation, the pace of development, is unlike ever before,” she says. “There are new statistical methods, new kinds of data, new model structures.”…(More)”.

Public Sector Tech: New tools for the new normal

Curated on Posted on by Stefaan Verhulst

Special issue by ZDNet exploring “how new technologies like AI, cloud, drones, and 5G are helping government agencies, public organizations, and private companies respond to the events of today and tomorrow…:

Exploring Digital Government Transformation in the EU – Understanding public sector innovation in a data-driven society

Curated on Posted on by Stefaan Verhulst

Report edited by Misuraca, G., Barcevičius, E. and Codagnone, C.: “This report presents the final results of the research “Exploring Digital Government Transformation in the EU: understanding public sector innovation in a data-driven society”, in short DigiGov. After introducing the design and methodology of the study, the report provides a summary of the findings of the comprehensive analysis of the state of the art in the field, conducted reviewing a vast body of scientific literature, policy documents and practitioners generated reports in a broad range of disciplines and policy domains, with a focus on the EU. The scope and key dimensions underlying the development of the DigiGov-F conceptual framework are then presented. This is a theory-informed heuristic instrument to help mapping the effects of Digital Government Transformation and able to support defining change strategies within the institutional settings of public administration. Further, the report provides an overview of the findings of the empirical case studies conducted, and employing experimental or quasi-experimental components, to test and refine the conceptual framework proposed, while gathering evidence on impacts of Digital Government Transformation, through identifying real-life drivers and barriers in diverse Member States and policy domains. The report concludes outlining future research and policy recommendations, as well as depicting possible scenarios for future Digital Government Transformation, developed as a result of a dedicated foresight policy lab. This was conducted as part of the expert consultation and stakeholder engagement process that accompanied all the phases of the research implementation. Insights generated from the study also serve to pave the way for further empirical research and policy experimentation, and to contribute to the policy debate on how to shape Digital Europe at the horizon 2040….(More)”.

Quantified Storytelling: A Narrative Analysis of Metrics on Social Media

Curated on Posted on by Stefaan Verhulst

Book by Alex Georgakopoulou, Stefan Iversen and Carsten Stage: “This book interrogates the role of quantification in stories on social media: how do visible numbers (e.g. of views, shares, likes) and invisible algorithmic measurements shape the stories we post and engage with? The links of quantification with stories have not been explored sufficiently in storytelling research or in social media studies, despite the fact that platforms have been integrating sophisticated metrics into developing facilities for sharing stories, with a massive appeal to ordinary users, influencers and businesses alike.

With case-studies from Instagram, Reddit and Snapchat, the authors show how three types of metrics, namely content metrics, interface metrics and algorithmic metrics, affect the ways in which cancer patients share their experiences, the circulation of specific stories that mobilize counter-publics and the design of stories as facilities on platforms. The analyses document how numbers structure elements in stories, indicate and produce engagement and become resources for the tellers’ self-presentation….(More)”.

Improving data access democratizes and diversifies science

Curated on Posted on by Stefaan Verhulst

Research article by Abhishek Nagaraj, Esther Shears, and Mathijs de Vaan: “Data access is critical to empirical research, but past work on open access is largely restricted to the life sciences and has not directly analyzed the impact of data access restrictions. We analyze the impact of improved data access on the quantity, quality, and diversity of scientific research. We focus on the effects of a shift in the accessibility of satellite imagery data from Landsat, a NASA program that provides valuable remote-sensing data. Our results suggest that improved access to scientific data can lead to a large increase in the quantity and quality of scientific research. Further, better data access disproportionately enables the entry of scientists with fewer resources, and it promotes diversity of scientific research….(More)”

Research 4.0: research in the age of automation

Curated on Posted on by Stefaan Verhulst

Report by Rob Procter, Ben Glover, and Elliot Jones: “There is a growing consensus that we are at the start of a fourth industrial revolution, driven by developments in Artificial Intelligence, machine learning, robotics, the Internet of Things, 3-D printing, nanotechnology, biotechnology, 5G, new forms of energy storage and quantum computing. This report seeks to understand what impact AI is having on the UK’s research sector and what implications it has for its future, with a particular focus on academic research.

Building on our interim report, we find that AI is increasingly deployed in academic research in the UK in a broad range of disciplines. The combination of an explosion of new digital data sources with powerful new analytical tools represents a ‘double dividend’ for researchers. This is allowing researchers to investigate questions that would have been unanswerable just a decade ago. Whilst there has been considerable take-up of AI in academic research, the report highlights that steps could be taken to ensure even wider adoption of these new techniques and technologies, including wider training in the necessary skills for effective utilisation of AI, faster routes to culture change and greater multi-disciplinary collaboration.

This report recognises that the Covid-19 pandemic means universities are currently facing significant pressures, with considerable demands on their resources whilst simultaneously facing threats to income. But as we emerge from the current crisis, we urge policy makers and universities to consider the report’s recommendations and take steps to fortify the UK’s position as a place of world-leading research. Indeed, the current crisis has only reminded us of the critical importance of a highly functioning and flourishing research sector. The report recommends:

The current post-16 curriculum should be reviewed to ensure all pupils receive a grounding in basic digital, quantitative and ethical skills necessary to ensure the effective and appropriate utilisation of AI.A UK-wide audit of research computing and data infrastructure provision is conducted to consider how access might be levelled up.

UK Research and Innovation (UKRI) should consider incentivising institutions to utilise AI wherever it can offer benefits to the economy and society in their future spending on research and development.

Universities should take steps to ensure that it is easier for researchers to move between academia and industry, for example, by putting less emphasis on publications, and recognise other outputs and measures of achievement when hiring for academic posts….(More)”.

Sortition, its advocates and its critics: An empirical analysis of citizens’ and MPs’ support for random selection as a democratic reform proposal

Curated on Posted on by Stefaan Verhulst

Paper by Vincent Jacquet et al: “This article explores the prospects of an increasingly debated democratic reform: assigning political offices by lot. While this idea is advocated by political theorists and politicians in favour of participatory and deliberative democracy, the article investigates the extent to which citizens and MPs actually endorse different variants of ‘sortition’. We test for differences among respondents’ social status, disaffection with elections and political ideology. Our findings suggest that MPs are largely opposed to sortitioning political offices when their decision-making power is more than consultative, although leftist MPs tend to be in favour of mixed assemblies (involving elected and sortitioned members). Among citizens, random selection seems to appeal above all to disaffected individuals with a lower social status. The article ends with a discussion of the political prospects of sortition being introduced as a democratic reform…(More).”

The Cruel New Era of Data-Driven Deportation

Curated on Posted on by Stefaan Verhulst

Article by Alvaro M. Bedoya: “For a long time, mass deportations were a small-data affair, driven by tips, one-off investigations, or animus-driven hunches. But beginning under George W. Bush, and expanding under Barack Obama, ICE leadership started to reap the benefits of Big Data. The centerpiece of that shift was the “Secure Communities” program, which gathered the fingerprints of arrestees at local and state jails across the nation and compared them with immigration records. That program quickly became a major driver for interior deportations. But ICE wanted more data. The agency had long tapped into driver address records through law enforcement networks. Eyeing the breadth of DMV databases, agents began to ask state officials to run face recognition searches on driver photos against the photos of undocumented people. In Utah, for example, ICE officers requested hundreds of face searches starting in late 2015. Many immigrants avoid contact with any government agency, even the DMV, but they can’t go without heat, electricity, or water; ICE aimed to find them, too. So, that same year, ICE paid for access to a private database that includes the addresses of customers from 80 national and regional electric, cable, gas, and telephone companies.

Amid this bonanza, at least, the Obama administration still acknowledged red lines. Some data were too invasive, some uses too immoral. Under Donald Trump, these limits fell away.

In 2017, breaking with prior practice, ICE started to use data from interviews with scared, detained kids and their relatives to find and arrest more than 500 sponsors who stepped forward to take in the children. At the same time, ICE announced a plan for a social media monitoring program that would use artificial intelligence to automatically flag 10,000 people per month for deportation investigations. (It was scuttled only when computer scientists helpfully indicated that the proposed system was impossible.) The next year, ICE secured access to 5 billion license plate scans from public parking lots and roadways, a hoard that tracks the drives of 60 percent of Americans—an initiative blocked by Department of Homeland Security leadership four years earlier. In August, the agency cut a deal with Clearview AI, whose technology identifies people by comparing their faces not to millions of driver photos, but to 3 billion images from social media and other sites. This is a new era of immigrant surveillance: ICE has transformed from an agency that tracks some people sometimes to an agency that can track anyone at any time….(More)”.