Autumm Zellers-Leon

Research

Health-related fake news during the COVID-19 pandemic: perceived trust and information search | Emerald Insight

by Lei Zheng, Jon D. Elhai, Miao Miao, Yu Wang, Yiwen Wang, and Yiqun Gan

The authors' results underscore the important role of PADM in understanding Internet users' trust in and search for HOFN. When people trust HOFN, they may seek more information to implement further protective actions. Importantly, it appears that trust in HOFN varies with environmental cues (regional pandemic severity) and with individuals' perceived control, providing insight into developing coping strategies during a pandemic. Source: Health-related fake news during the COVID-19 pandemic: perceived trust and information search | Emerald Insight

January 17, 2022

Research

Impact of Information Communication Technology on labor productivity: A panel and cross-sectional analysis | ScienceDirect

by Yash Laddha, Aviral Tiwari, Rafał Kasperowicz, Yuriy Bilan, and Dalia Streimikiene

This article examines the contribution of information and communications technologies (ICT) to labor productivity using panel data approach. The study covers the period of 2000–2015 for a complete dataset of 98 countries as well for three selected groups: low-income, middle-income, and high-income countries. The findings imply that telephone subscription and broadband subscription have a significant impact on overall labor productivity as well as labor productivity of service sector. The ICT affects the labor productivity, so investing in Information Communication Technology is necessary to increase the labor productivity. Source: Impact of Information Communication Technology on labor productivity: A panel and cross-sectional analysis | ScienceDirect

January 17, 2022

Analysis

Chicago’s “Race-Neutral” Traffic Cameras Ticket Black and Latino Drivers the Most | ProPublica

by Laila Milevski

When then-Mayor Richard M. Daley ushered in Chicago’s red-light cameras nearly two decades ago, he said they would help the city curb dangerous driving. “This is all about safety, safety of pedestrians, safety of other drivers, passengers, everyone,” he said. His successors echoed those sentiments as they expanded camera enforcement. “My goal is only one thing, the safety of our kids,” Rahm Emanuel said in 2011, as he lobbied for the introduction of speed cameras. And in 2020, Lori Lightfoot assured residents her expansion of the program was “about making sure that we keep communities safe.” But for all of their safety benefits, the hundreds of cameras that dot the city — and generate tens of millions of dollars a year for City Hall — have come at a steep cost for motorists from the city’s Black and Latino neighborhoods. A ProPublica analysis of millions of citations found that households in majority Black and Hispanic ZIP codes received tickets at around twice the rate of those in white areas between 2015 and 2019. Source: Chicago’s “Race-Neutral” Traffic Cameras Ticket Black and Latino Drivers the Most | ProPublica

January 17, 2022

Research

Digital phenotyping and the (data) shadow of Alzheimer’s disease | Big Data and Society

by Richard Milne, Alessia Costa, and Natassia Brenman,

In this paper, we examine the practice and promises of digital phenotyping. We build on work on the ‘data self’ to focus on a medical domain in which the value and nature of knowledge and relations with data have been played out with particular persistence, that of Alzheimer's disease research. Drawing on research with researchers and developers, we consider the intersection of hopes and concerns related to both digital tools and Alzheimer's disease using the metaphor of the ‘data shadow’. We suggest that as a tool for engaging with the nature of the data self, the shadow is usefully able to capture both the dynamic and distorted nature of data representations, and the unease and concern associated with encounters between individuals or groups and data about them. We then consider what the data shadow ‘is’ in relation to ageing data subjects, and the nature of the representation of the individual's cognitive state and dementia risk that is produced by digital tools. Second, we consider what the data shadow ‘does’, through researchers and practitioners’ discussions of digital phenotyping practices in the dementia field as alternately empowering, enabling and threatening. Source: Digital phenotyping and the (data) shadow of Alzheimer’s disease | Big Data and Society

January 17, 2022

Research

The Chilling Effects of Digital Dataveillance: A Theoretical Model and an Empirical Research Agenda | Big Data and Society

by Moritz Büchi, Noemi Festic, and Michael Latzer

People's sense of being subject to digital dataveillance can cause them to restrict their digital communication behavior. Such a chilling effect is essentially a form of self-censorship in everyday digital media use with the attendant risks of undermining individual autonomy and well-being. This article combines the existing theoretical and limited empirical work on surveillance and chilling effects across fields with an analysis of novel data toward a research agenda. The institutional practice of dataveillance—the automated, continuous, and unspecific collection, retention, and analysis of digital traces—affects individual behavior. A mechanism-based causal model based on the theory of planned behavior is proposed for the micro level: An individual's increased sense of dataveillance causes their subjective probability assigned to negative outcomes of digital communication behavior to increase and attitudes toward this communication to become less favorable, ultimately decreasing the intention to engage in it. In aggregate and triggered through successive salience shocks such as data scandals, dataveillance is accordingly hypothesized to lower the baseline of free digital communication in a society through the chilling effects mechanism. From the developed theoretical model, a set of methodological consequences and questions for future studies are derived. Source: The Chilling Effects of Digital Dataveillance: A Theoretical Model and an Empirical Research Agenda | Big Data and Society

January 17, 2022

Analysis

How a racialized disinformation campaign ties itself to The 1619 Project | Columbia Journalism Review

by Brandi Collins-Dexter and Joan Donovan

Footage of the January 6 Capitol insurrection revealed hundreds of references to 1776—in signs and in speeches, on t-shirts and hats and stickers. “1776” was chanted in the Capitol halls by leading figures within the so-called alt-right, including some who had also participated in the racist riot in Charlottesville, Virginia, and by those who believed themselves participants in the dawn of the next American revolution. The Proud Boys, too, cite this date; they sell their merch through a store called 1776. We are researchers of media manipulation and disinformation at the Harvard Kennedy School’s Shorenstein Center, and we wanted to know more about how “1776” became the battle cry of the insurrection. Our research reveals that the popularity of “1776” owes in part to keyword squatting—a tactic by which right-wing media have dominated the keywords “1619” and “critical race theory” and enabled a racialized disinformation campaign, waged by Trump and his acolytes, against Black civil rights gains. Source: How a racialized disinformation campaign ties itself to The 1619 Project | Columbia Journalism Review

January 17, 2022

Research

Algorithmic reparation | Big Data and Society

by Jenny L. Davis, Apryl Williams, and Michael W. Yang

Machine learning algorithms pervade contemporary society. They are integral to social institutions, inform processes of governance, and animate the mundane technologies of daily life. Consistently, the outcomes of machine learning reflect, reproduce, and amplify structural inequalities. The field of fair machine learning has emerged in response, developing mathematical techniques that increase fairness based on anti-classification, classification parity, and calibration standards. In practice, these computational correctives invariably fall short, operating from an algorithmic idealism that does not, and cannot, address systemic, Intersectional stratifications. Taking present fair machine learning methods as our point of departure, we suggest instead the notion and practice of algorithmic reparation. Rooted in theories of Intersectionality, reparative algorithms name, unmask, and undo allocative and representational harms as they materialize in sociotechnical form. We propose algorithmic reparation as a foundation for building, evaluating, adjusting, and when necessary, omitting and eradicating machine learning systems. Source: Algorithmic reparation | Big Data and Society

January 17, 2022

News

A Move for ‘Algorithmic Reparation’ Calls for Racial Justice in AI | WIRED

by Khari Johnson

Supporters of algorithmic reparation suggest taking lessons from curation professionals such as librarians, who’ve had to consider how to ethically collect data about people and what should be included in libraries. They propose considering not just whether the performance of an AI model is deemed fair or good but whether it shifts power. The suggestions echo earlier recommendations by former Google AI researcher Timnit Gebru, who in a 2019 paper encouraged machine learning practitioners to consider how archivists and library sciences dealt with issues involving ethics, inclusivity, and power. Gebru says Google fired her in late 2020, and recently launched a distributed AI research center. A critical analysis concluded that Google subjected Gebru to a pattern of abuse historically aimed at Black women in professional environments. Authors of that analysis also urged computer scientists to look for patterns in history and society in addition to data. Earlier this year, five US senators urged Google to hire an independent auditor to evaluate the impact of racism on Google’s products and workplace. Google did not respond to the letter. Source: A Move for ‘Algorithmic Reparation’ Calls for Racial Justice in AI | WIRED

January 17, 2022