Data Matters Ethics, Data, and International Research Collaboration in a Changing World: Proceedings of a Workshop (2018) / Chapter Skim
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1 Introduction and Overview
1 Introduction and Overview
Pages 1-16
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To examine international research collaborations in a systematic way, the Government-University-Industry Research Roundtable (GUIRR) launched a Working Group on International Research Collaborations (I-Group)
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In this third workshop in the series, representatives from around the world and from GUIRR's three constituent sectors -- government, university, and industry -- gathered to examine advisory principles for consideration when developing international research agreements, in the pursuit of highlighting promising practices for sustaining and enabling international research collaborations at the highest ethical level possible. The intent of the workshop was to explore, through an ethical lens, the changing opportunities and risks associated with data management and use across disciplinary domains -- all within the context of international research agreements.
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The organization of the proceedings follows that of the workshop by focusing on the "core elements" of international research collaborations identified in the Planning Committee charge. The goal for the workshop and the proceedings is to serve as an informational resource for participants and others interested in international research collaborations.
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Ethical Considerations To set the stage for the workshop's discussions, Jacob Metcalf, researcher at the Data & Society Research Institute and founding partner of Ethical Resolve, spoke about how the processes and infrastructures that enable rigorous thinking about data ethics differ from the norms and infrastructures developed over the past 70 years to deal with research ethics. The latter, he said, were built to handle very different types of harms and address various kinds of human subjects.
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In the context of data science, the Common Rule defines research as the creation of new data in pursuit of generalized knowledge and it defines human subjects as individuals in whose lives or bodies a researcher intervenes in the collection of the data, explained Metcalf. He noted that the Common Rule is not the end-all and be-all of research ethics, and it does have shortcomings, but it has become the touchstone for ethics training within the nation's universities.
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The hands-on, practical elements of the research ethics established by the Common Rule are about resolving the core risk to human subjects that their autonomy will be compromised, and they will not receive adequate care if the researcher role dominates. The application of the Common Rule to other research fields means that the previously established social roles, obligations, and epistemologies respected in other research fields must fit under a set of rules that were designed for a very particular historical task of addressing the conflict of being a researcher physician, explained Metcalf.
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Second, it is important to recognize that data sharing is always also model sharing, so a data sharing agreement is about moving models and interpretations between collaborators as much as it is about moving spreadsheets back and forth. "If you are building a deep neural network off of a data set, you are also sharing what a machine will learn from that data set, so the assumptions about who the research subjects are and what we can know about them travels with that human subject data in complex and subtle ways," he said.
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As a final comment, he reiterated his earlier message: "We need to be looking at the loop between norms and infrastructures," he said. Examples of Multinational Collaborations To illustrate how some of the challenges Metcalf described play out in actual international collaborations, Ghassem Asrar, director of the Joint Global Change Research Institute of the Pacific Northwest National Laboratory, presented three examples from the world of environmental monitoring, modeling, and prediction.
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Asrar listed several scientific and technical challenges that were considered when defining the data sharing policies and practices that the nations participating in global environmental studies had to agree on so that the knowledge derived from those data would be useful for its intended purpose. These challenges included the multiple scales of time and space over which the data are generated and multiple sources of those data; the complex nature of the system and the feedbacks among its components; the uncertainty in the measurements and analyses; the need for data validation, quality assurance, curation, stewardship, dissemination, and sharing; national differences in computation, visualization, and analytical capabilities; and a lack of data, particularly regarding socioeconomics.
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The World Meteorological Organization (WMO) , the United Nations' specialized agency on weather, climate, and water, has existed in some form since 1873, and it coordinates the work of 200,000 national meteorological and hydrological experts and a global observing network of more than 10,000 stations and operational weather satellites.
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One project ESGF has enabled has been to model the geography of food, water, and energy globally to identify potential hotspots that will not be able to produce enough food to meet local demand. This modeling exercise calculated that humans in North America use approximately 30 percent of the terrestrial ecosystem's supply of net primary production, while South America uses 8 percent,
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"Clearly, science can and should play a major role in this process and not only in creating a system but making sure that the integrity of what results from these systems are maintained and are used in the way that were intended to be used." As examples, he said that the role of research in data development can include providing advice on the best data sets to use for various purposes, as well as the merits and limitations of those data sets. Research can also identify highpriority research needs, promote sound data stewardship, help make data sets accessible and usable, and promote data quality and uncertainty characterization.
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Asrar was asked if he had examples of two countries responding to a common challenge and acting together, and he replied that development organizations are funding and enticing countries to come together to tackle challenges related to food and water. His organization is supporting an effort in South America that has developed an agreement involving Chile, Uruguay, Argentina, Columbia, and others in the region to tackle problems of energy, food, and water using data and modeling contributed by countries with the technological capacity to assist these efforts.
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The solution the Census Bureau deployed at the time, he explained, was to use a technique tested by the Census Bureau's statistical research division called swapping, which swaps data from households from one location with households with identical characteristics on a certain set of variables from a different geographical location. Which households were swapped is not public information, nor is the list of characteristics used to identify which households to swap, and the selection process was targeted to affect the records that were most at risk of disclosure.
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The penalty for releasing those data is $600,000 and 6 years in prison, so everyone at the Census Bureau takes the responsibility of securing those data seriously. He added that the Census Bureau is going to release the source code for its software, which has never been done before, to allow researchers to see how differential privacy affects accuracy versus privacy using publicly released data from the 1930 and 1940 censuses.
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Garfinkel noted in response to a question that differential privacy is not applicable to clinical trials data for several reasons. The first is that clinical trials data contain text elements, which do not work well with privacy mechanisms based on mathematics.
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