Publications by Year: 2018

2018
The Public Mapping ProjectHow Public Participation Can Revolutionize Redistricting
McDonald M, Altman M. The Public Mapping ProjectHow Public Participation Can Revolutionize Redistricting. Cornell University Press; 2018. Publisher's VersionAbstract

The Laurence and Lynne Brown Democracy Medal is an initiative of the McCourtney Institute for Democracy at Pennsylvania State University. It annually recognizes outstanding individuals, groups, and organizations that produce exceptional innovations to further democracy in the United States or around the world.

Micah Altman and Michael P. McDonald unveil the Public Mapping Project, which developed DistrictBuilder, an open-source software redistricting application designed to give the public transparent, accessible, and easy-to-use online mapping tools. As they show, the goal is for all citizens to have access to the same information that legislators use when drawing congressional maps—and use that data to create maps of their own.

Differential Privacy: A Primer for a Non-Technical Audience
Wood A, Altman M, Bembenek A, Bun M, Gaboardi M, Honaker J, O'Brien DR, Steinke T, Vadhan S. Differential Privacy: A Primer for a Non-Technical Audience. Vanderbilt Journal of Entertainment and Technology Law (JETlaw) [Internet]. 2018;21 :209-276. Publisher's VersionAbstract

ifferential privacy is a formal mathematical framework for quantifying and managing privacy risks. It provides provable privacy protection against a wide range of potential attacks, including those currently unforeseen. Differential privacy is primarily studied in the context of the collection, analysis, and release of aggregate statistics. These range from simple statistical estimations, such as averages, to machine learning. Tools for differentially private analysis are now in early stages of implementation and use across a variety of academic, industry, and government settings. Interest in the concept is growing among potential users of the tools, as well as within legal and policy communities, as it holds promise as a potential approach to satisfying legal requirements for privacy protection when handling personal information. In particular, differential privacy may be seen as a technical solution for analyzing and sharing data while protecting the privacy of individuals in accordance with existing legal or policy requirements for de-identification or disclosure limitation.

 

 

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A Grand Challenges-Based Research Agenda for Scholarly Communication and Information Science
Altman M, Bourg C, Cohen P, Choudhury GS, Henry C, Kriegsman S, Minow M, Selematsela D, Sengupta A, Suber P, et al. A Grand Challenges-Based Research Agenda for Scholarly Communication and Information Science.; 2018. Publisher's VersionAbstract
The “Grand Challenges-Based Research Agenda for Scholarly Communication and Information Science” describes a vision for a more inclusive, open, equitable, and sustainable future for scholarship; characterizes the central technical, organizational, and institutional barriers to this future; describes the areas research needs to advance this future; and identifies targeted “grand challenge” research problems for knowledge generation. These “grand challenges” are fundamental research problems with broad applications, whose solutions are potentially achievable within the next decade.
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Chassanoff A, Borghi J, AlNoamany Y, Thornton K. Software Curation in Research Libraries: Practice and Promise. he Journal of Librarianship and Scholarly Communication. 2018;Forthcoming.Abstract

INTRODUCTION. Research software plays an increasingly vital role in the scholarly record. Academic research libraries are in the early stages of exploring strategies for curating and preserving research software, aiming to provide long-term access and use. DESCRIPTION OF PROGRAM. In 2016, the Council on Library and Information Resources (CLIR) began offering postdoctoral fellowships in software curation. Four institutions hosted the initial cohort of software curation fellows. This article describes the work activities and research program of the cohort, highlighting the challenges and benefits of doing this exploratory work in research libraries. NEXT STEPS. Academic research libraries are poised to play an important role in research and development around robust services for software curation. The next cohort of CLIR fellows are set to begin in fall 2018 and will likely shape and contribute substantially to an emergent research agenda.

softwarecuration-preprint.pdf
How big data challenges privacy, and how science can help. The Washington DC 100 [Internet]. 2018;May 8. Publisher's VersionAbstract
The collection of personal information has become broader and more threatening than anyone could have imagined. Our research finds traditional approaches to safeguarding privacy are stretched to the limit as thousands of data points are collected about us every day and maintained indefinitely by a host of technology platforms.
how_big_data_challenges_privacy_and_how_science_can_help_-_the_washington_dc_100.pdf
Altman M, Cohen A, Fluitt A, Nissim K, Washington M, Wood A. Comments on new techniques and Methodologies for Combining Data From Multiple Source. Office of Management and Budget. 2018.Abstract

Comments in response to  Request for information,

New techniques and methodologies based on combining data from multiple sources

outline_of_omb_rfi_response.pdf
Altman M, Wood A. How big data challenges privacy, and how science can help. Washingto DC 100 [Internet]. 2018;May. Publisher's VersionAbstract

The collection of personal information has become broader and more threatening than anyone could have imagined. Our research finds traditional approaches to safeguarding privacy are stretched to the limit as thousands of data points are collected about us every day and maintained indefinitely by a host of technology platforms.

Altman M, Vayena E, Wood A. A Harm-Reduction Framework for Algorithmic Fairness. IEEE Privacy and Security. 2018;Forthcoming.Abstract

In this article we recognize the profound effects that algorithmic decision-making can have on people’s lives and proposes a harm-reduction framework for algorithmic fairness. We argue that any evaluation of algorithmic fairness must take into account the foreseeable effects that algorithmic design, implementation, and use have on the well-being of individuals. We further demonstrate how counterfactual frameworks for causal inference developed in statistics and computer science can be used as the basis for defining and estimating the foreseeable effects of algorithmic decisions. Finally, we argue that certain patterns of foreseeable harms are unfair. An algorithmic decision is unfair if it imposes predictable harms on sets of individuals that are unconsciously disproportionate to the benefits these same decisions produce elsewhere. Also, an algorithmic decision is unfair when it is regressive, i.e., when members of disadvantaged groups pay a higher cost for the social benefits of that decision.

algorithmicharm.pdf
Hellyar D, Walsh R, Altman M. Improving digital experience through modeling the human experience: The resurgence of ‘Virtual’- (& ‘Augmented’- & ‘Mixed’-) Reality. In: Reconceptualizing Libraries. Routledge Press ; 2018.Abstract

This essay is designed generally to introduce information professionals and researchers to the topic of VR, to characterize its potential to enhance human experiences, and to identify the concepts that are critical to its application. The essay is also intended specifically for professional librarians, and applied library information science researchers, who aim to integrate new interface technologies and design concepts into library systems.

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Nissim K, Steinke T, Wood A, Altman M, Bembenek A, Bun M, Gaboardi M, O'Brien DR, Vadhan S. Differential Privacy: A Primer for a Non-Technical Audience. Vanderbilt Journal of Entertainment and Technology Law. 2018;Forthcoming.Abstract

Differential privacy is a formal mathematical formal mathematical framework for guaranteeing privacy protection when analyzing or releasing statistical data. Recently emerging from the theoretical computer science literature, differential privacy is now in initial stages of implementation and use in various academic, industry, and government settings.

This document is a primer on differential privacy. Using intuitive illustrations and limited mathematical formalism, this primer provides an introduction to dierential privacy for non-technical practitioners, who are increasingly tasked with making decisions with respect to dierential privacy as it grows more widespread in use. In particular, the examples in this document illustrate ways in which social science and legal audiences can conceptualize the guarantees provided by differetial privacy with respect to the decisions they make when managing personal data about research subjects and informing them about the privacy protection they will be afforded.

nissim_et_al_-_differential_privacy_primer_for_non-technical_audiences_1.pdf
Altman M, Wood A, O'Brien D, Gasser U. Practical Approaches to Big Data Privacy Over Time. International Journal of Data Privacy Law [Internet]. 2018. Earlier versionAbstract

Increasingly, governments and businesses are collecting, analyzing, and sharing detailed information about individuals over long periods of time. Vast quantities of data from new sources and novel methods for large-scale data analysis promise to yield deeper understanding of human characteristics, behavior, and relationships and advance the state of science, public policy, and innovation. At the same time, the collection and use of fine-grained personal data over time is associated with significant risks to individuals, groups, and society at large. In this article, we examine a range of longterm data collections, conducted by researchers in social science, in order to identify the characteristics of these programs that drive their unique sets of risks and benefits. We also examine the practices that have been established by social scientists to protect the privacy of data subjects in light of the challenges presented in long-term studies. We argue that many uses of big data, across academic, government, and industry settings, have characteristics similar to those of traditional long-term research studies. In this article, we discuss the lessons that can be learned from longstanding data management practices in research and potentially applied in the context of newly emerging data sources and uses.

altman-et-al.-practical-approaches-to-big-data-privacy-over-time_1.pdf