Project Overview

An increasing number of research disciplines and industry leaders embrace big data approaches as they pursue research questions and project development. However, the methods used to assemble large datasets, and their applications in decision-making contexts, challenge existing ethical paradigms for data management, data integrity, human subject protections, and data use. In many fields, for example, aggregating data from different sources can make privacy protections for human subjects more complex, and raise questions about data ownership. Unfortunately, current attempts to identify and address these challenges are often focused within specific disciplines or corporate settings and offer little opportunity to integrate these evolving ethical concerns within master's and doctoral programs. 

To address the increasing use of big data in graduate student research and to prepare graduate deans as leaders in graduate training within their institutions, CGS and its partner, PERVADE (“Pervasive Data Ethics for Computational Research”), received funding from the Office of Research Integrity (ORI) and Elsevier to host a virtual workshop April 12-14, 2021. This workshop convened thought leaders from the big data ethics community and graduate deans from research-intensive institutions.  The goals of the workshop were to identify ways graduate deans can augment and influence the training of graduate students in meeting the challenges of using big data methods in their research. Workshop goals included identifying specific ethical challenges that arise from the use of big data methods in graduate student research, critiquing existing resources for training, identifying potential levers for change, and formulating strategies for deploying and embedding resources for big data ethics within the RCR training curriculum. A workshop summary report will be released in September 2021.

Press Release

CGS and PERVADE to Convene Thought Leaders on Ethical Issues in Big Data Research

Conference Resources
 

Recordings for each of the three plenary sessions are available to view at the links below. Brief descriptions of each plenary are included under the session title.

• "The Challenge of Training in Big Data Ethics: Why is it Hard?" by Michael Zimmer (Marquette University)

The use of big databases and sophisticated data analytics has allowed detailed insight into a substantial variety of human activities. The development of these resources and adoption of these approaches has become widespread among both commercial and academic research enterprises.  In this presentation, Dr. Zimmer points out that accumulating data about people is easy and pervasive, yet there are no widely-used standards to insure individual privacy, consent for human subjects research, or assessment of the potential harms that may result. Dr. Zimmer provides some potential targets for better training of graduate students and other researchers in the ethical use of these methods and resources.

• "The Challenge of Training in Big Data Ethics: What Resources are Available or are Needed?" by Casey Fiesler (University of Colorado)

What are the resources that are available or that are needed as we engage graduate students in considering the pitfalls and potential biases that may exist in the collection and use of big data sets in research? In this keynote, Dr. Fiesler described these resources and the challenges to introducing them effectively within disciplinary training.  Her thesis is that embedding ethics discussions and activities within the academic training for researchers in many domains integrates these issues within a disciplinary context and prevents the siloing of “ethics” from research.  Dr. Fiesler suggests a set of priorities for faculty and other leaders as they identify opportunities within the graduate training curriculum to engage students in ethical concerns. 

• "Expanding Graduate Training in RCR: Big Data Ethics" by Thomas Jeitschko (Michigan State University)

Research integrity is fundamental to scholarship and creative work. Training in academic integrity should affirm and emphasize best practices, not just compliance and rule following. Ethics training should include content and examples that embed the training within the professional standards of the discipline.  Big data is increasingly used in many disciplines and the ethical use of these methods present the additional challenges of biased data, unanticipated harms that may arise from data release, and undercutting privacy concerns of individuals.  Dr. Jeitschko summarizes these potential issues and ways to engage faculty and other leaders in discussing how to address them in graduate student training within and across disciplines.

Visit the Elsevier website to access several relevant reports:

• Artificial Intelligence:  How knowledge is created, transferred, and used
• Research Futures: Drivers and scenarios for the next decade
• Gender Report 2020 - The Researcher Journey Through a Gender Lens
• The Power of Data to Advance the SDGs:  Mapping research for the Sustainable Development Goals
• Future of Research insight report

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