Open Data, Grey Data, and Stewardship: Universities at the Privacy Frontier

As universities recognize the inherent value in the data they collect and hold, they encounter unforeseen challenges in stewarding those data in ways that balance accountability, transparency, and protection of privacy, academic freedom, and intellectual property. Two parallel developments in academic data collection are converging: (1) open access requirements, whereby researchers must provide access to their data as a condition of obtaining grant funding or publishing results in journals; and (2) the vast accumulation of 'grey data' about individuals in their daily activities of research, teaching, learning, services, and administration. The boundaries between research and grey data are blurring, making it more difficult to assess the risks and responsibilities associated with any data collection. Many sets of data, both research and grey, fall outside privacy regulations such as HIPAA, FERPA, and PII. Universities are exploiting these data for research, learning analytics, faculty evaluation, strategic decisions, and other sensitive matters. Commercial entities are besieging universities with requests for access to data or for partnerships to mine them. The privacy frontier facing research universities spans open access practices, uses and misuses of data, public records requests, cyber risk, and curating data for privacy protection. This paper explores the competing values inherent in data stewardship and makes recommendations for practice, drawing on the pioneering work of the University of California in privacy and information security, data governance, and cyber risk.Comment: Final published version, Sept 30, 201

Mapping the Current Landscape of Research Library Engagement with Emerging Technologies in Research and Learning: Final Report

The generation, dissemination, and analysis of digital information is a significant driver, and consequence, of technological change. As data and information stewards in physical and virtual space, research libraries are thoroughly entangled in the challenges presented by the Fourth Industrial Revolution:1 a societal shift powered not by steam or electricity, but by data, and characterized by a fusion of the physical and digital worlds.2 Organizing, structuring, preserving, and providing access to growing volumes of the digital data generated and required by research and industry will become a critically important function. As partners with the community of researchers and scholars, research libraries are also recognizing and adapting to the consequences of technological change in the practices of scholarship and scholarly communication. Technologies that have emerged or become ubiquitous within the last decade have accelerated information production and have catalyzed profound changes in the ways scholars, students, and the general public create and engage with information. The production of an unprecedented volume and diversity of digital artifacts, the proliferation of machine learning (ML) technologies,3 and the emergence of data as the “world’s most valuable resource,”4 among other trends, present compelling opportunities for research libraries to contribute in new and significant ways to the research and learning enterprise. Librarians are all too familiar with predictions of the research library’s demise in an era when researchers have so much information at their fingertips. A growing body of evidence provides a resounding counterpoint: that the skills, experience, and values of librarians, and the persistence of libraries as an institution, will become more important than ever as researchers contend with the data deluge and the ephemerality and fragility of much digital content. This report identifies strategic opportunities for research libraries to adopt and engage with emerging technologies,5 with a roughly fiveyear time horizon. It considers the ways in which research library values and professional expertise inform and shape this engagement, the ways library and library worker roles will be reconceptualized, and the implication of a range of technologies on how the library fulfills its mission. The report builds on a literature review covering the last five years of published scholarship, primarily North American information science literature, and interviews with a dozen library field experts, completed in fall 2019. It begins with a discussion of four cross-cutting opportunities that permeate many or all aspects of research library services. Next, specific opportunities are identified in each of five core research library service areas: facilitating information discovery, stewarding the scholarly and cultural record, advancing digital scholarship, furthering student learning and success, and creating learning and collaboration spaces. Each section identifies key technologies shaping user behaviors and library services, and highlights exemplary initiatives. Underlying much of the discussion in this report is the idea that “digital transformation is increasingly about change management”6 —that adoption of or engagement with emerging technologies must be part of a broader strategy for organizational change, for “moving emerging work from the periphery to the core,”7 and a broader shift in conceptualizing the research library and its services. Above all, libraries are benefitting from the ways in which emerging technologies offer opportunities to center users and move from a centralized and often siloed service model to embedded, collaborative engagement with the research and learning enterprise

Big data governance of personal health information and challenges to contextual integrity

Pervasive digitization and aggregation of personal health information (PHI), along with artificial intelligence (AI) and other advanced analytical techniques, hold promise of improved health and healthcare services. These advances also pose significant data governance challenges for ensuring value for individual, organizational, and societal stakeholders as well as individual privacy and autonomy. Through a case study of a controversial public-private partnership between Royal Free Trust, a National Health Service hospital system in the United Kingdom, and Alphabet’s AI venture DeepMind Health, we investigate how forms of data governance were adapted, as PHI data flowed into new use contexts, to address concerns of contextual integrity, which is violated when personal information collected in one use context moves to another use context with different norms of appropriateness

FAIR2: A framework for addressing discrimination bias in social data science

[EN] Building upon the FAIR principles of (meta)data (Findable, Accessible, Interoperable and Reusable) and drawing from research in the social, health, and data sciences, we propose a framework -FAIR2 (Frame, Articulate, Identify, Report) - for identifying and addressing discrimination bias in social data science. We illustrate how FAIR2 enriches data science with experiential knowledge, clarifies assumptions about discrimination with causal graphs and systematically analyzes sources of bias in the data, leading to a more ethical use of data and analytics for the public interest. FAIR2 can be applied in the classroom to prepare a new and diverse generation of data scientists. In this era of big data and advanced analytics, we argue that without an explicit framework to identify and address discrimination bias, data science will not realize its potential of advancing social justice.This work was generously funded by grant #015865 from the Public Interest Technology University Network - New America Foundation.Richter, F.; Nelson, E.; Coury, N.; Bruckman, L.; Knighton, S. (2023). FAIR2: A framework for addressing discrimination bias in social data science. Editorial Universitat Politècnica de València. 327-335. https://doi.org/10.4995/CARMA2023.2023.1640032733

Sustaining Software Preservation Efforts Through Use and Communities of Practice

The brief history of software preservation efforts illustrates one phenomenon repeatedly: not unlike spinning a plate on a broomstick, it is easy to get things going, but difficult to keep them stable and moving. Within the context of video games and other forms of cultural heritage (where most software preservation efforts have lately been focused), this challenge has several characteristic expressions, some technical (e.g., the difficulty of capturing and emulating protected binary files and proprietary hardware), and some legal (e.g., providing archive users with access to preserved games in the face of variously threatening end user licence agreements). In other contexts, such as the preservation of research-oriented software, there can be additional challenges, including insufficient awareness and training on unusual (or even unique) software and hardware systems, as well as a general lack of incentive for preserving “old data.” We believe that in both contexts, there is a relatively accessible solution: the fostering of communities of practice. Such groups are designed to bring together like-minded individuals to discuss, share, teach, implement, and sustain special interest groups—in this case, groups engaged in software preservation. In this paper, we present two approaches to sustaining software preservation efforts via community. The first is emphasizing within the community of practice the importance of “preservation through use,” that is, preserving software heritage by staying familiar with how it feels, looks, and works. The second approach for sustaining software preservation efforts is to convene direct and adjacent expertise to facilitate knowledge exchange across domain barriers to help address local needs; a sufficiently diverse community will be able (and eager) to provide these types of expertise on an as-needed basis. We outline here these sustainability mechanisms, then show how the networking of various domain-specific preservation efforts can be converted into a cohesive, transdisciplinary, and highly collaborative software preservation team. [This paper is a conference pre-print presented at IDCC 2020 after lightweight peer review.

Ethics of Artificial Intelligence in Education: Student Privacy and Data Protection

Rapid advances in artificial intelligence (AI) technology are profoundly altering human societies and lifestyles. Individuals face a variety of information security threats while enjoying the conveniences and customized services made possible by AI. The widespread use of AI in education has prompted widespread public concern regarding AI ethics in this field. The protection of pupil data privacy is an urgent matter that must be addressed. On the basis of a review of extant interpretations of AI ethics and student data privacy, this article examines the ethical risks posed by AI technology to student personal information and provides recommendations for addressing concerns regarding student data security

User perspectives on personalized account-based recommender systems

This research is focused on understanding user preferences for "my account"-based recommendations of library content. By interviewing users we have explored user attitudes about three areas of recommendation services; including 1) eliciting preferences for recommendation, 2) displaying recommendations, and 3) revising recommendations based on results. User interviews indicated a need for crafting recommender services in library settings with transparent functionality. Users requested that system designers make clear how recommendations are designed and provided. Further findings indicated a desire to use recommender systems to explore interdisciplinary research domains that have otherwise not been considered.Ope