https://hdl.handle.net/1721.1/121175 2026-04-05T02:05:02Z 2026-04-05T02:05:02Z McDonald, Michael P. Altman, Micah https://hdl.handle.net/1721.1/125437 2022-09-27T14:30:44Z 2018-01-01T00:00:00Z

The Public Mapping Project: How Public Participation Can Revolutionize Redistricting McDonald, Michael P.; Altman, Micah

2018-01-01T00:00:00Z Hellyar, Diana Walsh, Renee Altman, Micah https://hdl.handle.net/1721.1/125436 2022-09-28T16:38:23Z 2018-08-01T00:00:00Z

Improving Digital Experience Through Modeling the Human Experience The Resurgence of Virtual (and Augmented and Mixed) Reality Hellyar, Diana; Walsh, Renee; Altman, Micah This chapter 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 chapter 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. As authors, we are embedded in the research and practice of libraries. We have sought out both the literatures of interactions in virtual reality and the emerging technology of its implementation. This is new territory for libraries. So while we conjecture that the new affordances that VR and related technologies supply have substantial potential for libraries, the pathways are necessarily speculative.

2018-08-01T00:00:00Z Chassanoff, Alexandra M Altman, Micah https://hdl.handle.net/1721.1/125435 2022-09-30T12:09:16Z 2019-05-01T00:00:00Z

Curation as “Interoperability With the Future”: Preserving Scholarly Research Software in Academic Libraries Chassanoff, Alexandra M; Altman, Micah This article considers the problem of preserving research software within the wider realm of digital curation, academic research libraries, and the scholarly record. We conducted a pilot study to understand the ecosystem in which research software participates, and to identify significant characteristics that have high potential to support future scholarly practices. A set of topical curation dimensions were derived from the extant literature and applied to select cases of institutionally significant research software. This approach yields our main contribution, a curation model and decision framework for preserving research software as a scholarly object. The results of our study highlight the unique characteristics and challenges at play in building curation services in academic research libraries.

2019-05-01T00:00:00Z Okulicz-Kozaryn, Adam Altman, Micah https://hdl.handle.net/1721.1/125432 2022-09-27T15:41:11Z 2019-03-01T00:00:00Z

The Happiness-Energy Paradox: Energy Use is Unrelated to Subjective Well-Being Okulicz-Kozaryn, Adam; Altman, Micah Earth’s per capita energy use continues to grow, despite technological advances and widespread calls for reduction in energy consumption. The negative environmental consequences are well known: resource depletion, pollution, and global warming. However many remain reluctant to cut energy consumption because of the widespread, although, implicit, belief that a nation’s well being depends on its energy consumption. This article systematically examines the evidential support for the relationship between energy use and subjective well-being at the societal level, by integrating data from multiple sources, collected at multiple levels of government, and spanning four decades. This analysis reveals, surprisingly, that the most common measure of subjective well-being, life satisfaction, is unrelated to energy use -- whether measured at the national, state or county level. The nil relationship between happiness and energy use is reminiscent of the well-known Easterlin Paradox, however the causal mechanisms responsible to each remain in question. We discuss the possible causes for the Happiness-Energy paradox and potential policy implications.

2019-03-01T00:00:00Z Altman, Micah Wood, Alexandra Vayena, Effy https://hdl.handle.net/1721.1/125415 2022-09-30T00:37:42Z 2018-05-01T00:00:00Z

A Harm-Reduction Framework for Algorithmic Fairness Altman, Micah; Wood, Alexandra; Vayena, Effy In this article, we recognize the profound effects that algorithmic decision making can have on people's lives and propose 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 unconscionably disproportionate to the benefits these same decisions produce elsewhere. Also, an algorithmic decision is unfair when it is regressive, that is, when members of disadvantaged groups pay a higher cost for the social benefits of that decision.

2018-05-01T00:00:00Z Altman, Micah Wood, Alexandra O’Brien, David R Gasser, Urs https://hdl.handle.net/1721.1/125414 2022-09-27T22:27:47Z 2018-02-01T00:00:00Z

Practical approaches to big data privacy over time Altman, Micah; Wood, Alexandra; O’Brien, David R; Gasser, Urs Governments and businesses are increasingly collecting, analysing, 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, behaviour, and relationships and advance the state of science, public policy, and innovation. The collection and use of fine-grained personal data over time, at the same time, is associated with significant risks to individuals, groups, and society at large. This article examines a range of long-term research studies in order to identify the characteristics that drive their unique sets of risks and benefits and the practices established to protect research data subjects from long-term privacy risks. We find that many big data activities in government and industry settings have characteristics and risks similar to those of long-term research studies, but are subject to less oversight and control. We argue that the risks posed by big data over time can best be understood as a function of temporal factors comprising age, period, and frequency and non-temporal factors such as population diversity, sample size, dimensionality, and intended analytic use. Increasing complexity in any of these factors, individually or in combination, creates heightened risks that are not readily addressable through traditional de-identification and process controls. We provide practical recommendations for big data privacy controls based on the risk factors present in a specific case and informed by recent insights from the state of the art and practice.

2018-02-01T00:00:00Z Wood, Alexandra Airoldi, Edo Altman, Micah de Montandre, Yves Gasser, Urs O'Brien, David Vadhan, Salil https://hdl.handle.net/1721.1/121255 2019-06-15T03:01:03Z 2016-01-06T00:00:00Z

Privacy Tools project response to Common Rule Notice of Proposed Rule Making Wood, Alexandra; Airoldi, Edo; Altman, Micah; de Montandre, Yves; Gasser, Urs; O'Brien, David; Vadhan, Salil This is a Comment on the Department of Health and Human Services (HHS) Proposed Rule: Federal Policy for the Protection of Human Subjects. We recognize the exciting research opportunities enabled by new data sources and technologies for collecting, analyzing, and sharing data about individuals. With the ability to collect and analyze massive quantities of data related to human characteristics, behaviors, and interactions, researchers are increasingly able to explore phenomena in finer detail and with greater confidence. At the same time, a 2 major challenge for realizing the full potential of these recent advances will be protecting the privacy of human subjects. Approaches to privacy protection in common use in both research and industry contexts often provide limited real-world privacy protection. We believe institutional review boards (IRBs) and investigators require new guidance to inform their selection and implementation of appropriate measures for privacy protection in human subjects research.

2016-01-06T00:00:00Z