Improving fairness in machine learning systems: What do industry practitioners need?

The potential for machine learning (ML) systems to amplify social inequities and unfairness is receiving increasing popular and academic attention. A surge of recent work has focused on the development of algorithmic tools to assess and mitigate such unfairness. If these tools are to have a positive impact on industry practice, however, it is crucial that their design be informed by an understanding of real-world needs. Through 35 semi-structured interviews and an anonymous survey of 267 ML practitioners, we conduct the first systematic investigation of commercial product teams' challenges and needs for support in developing fairer ML systems. We identify areas of alignment and disconnect between the challenges faced by industry practitioners and solutions proposed in the fair ML research literature. Based on these findings, we highlight directions for future ML and HCI research that will better address industry practitioners' needs.Comment: To appear in the 2019 ACM CHI Conference on Human Factors in Computing Systems (CHI 2019

Data management of nanometre­ scale CMOS device simulations

In this paper we discuss the problems arising in managing and curating the data generated by simulations of nanometre scale CMOS (Complementary Metal–Oxide Semiconductor) transistors, circuits and systems and describe the software and operational techniques we have adopted to address them. Such simulations pose a number of challenges including, inter alia, multi­TByte data volumes, complex datasets with complex inter-relations between datasets, multi­-institutional collaborations including multiple specialisms and a mixture of academic and industrial partners, and demanding security requirements driven by commercial imperatives. This work was undertaken as part of the NanoCMOS project. However, the problems, solutions and experience seem likely to be of wider relevance, both within the CMOS design community and more generally in other disciplines

To share or not to share: Publication and quality assurance of research data outputs. A report commissioned by the Research Information Network

A study on current practices with respect to data creation, use, sharing and publication in eight research disciplines (systems biology, genomics, astronomy, chemical crystallography, rural economy and land use, classics, climate science and social and public health science). The study looked at data creation and care, motivations for sharing data, discovery, access and usability of datasets and quality assurance of data in each discipline

Curating E-Mails; A life-cycle approach to the management and preservation of e-mail messages

E-mail forms the backbone of communications in many modern institutions and organisations and is a valuable type of organisational, cultural, and historical record. Successful management and preservation of valuable e-mail messages and collections is therefore vital if organisational accountability is to be achieved and historical or cultural memory retained for the future. This requires attention by all stakeholders across the entire life-cycle of the e-mail records. This instalment of the Digital Curation Manual reports on the several issues involved in managing and curating e-mail messages for both current and future use. Although there is no 'one-size-fits-all' solution, this instalment outlines a generic framework for e-mail curation and preservation, provides a summary of current approaches, and addresses the technical, organisational and cultural challenges to successful e-mail management and longer-term curation.

Stewardship of the evolving scholarly record: from the invisible hand to conscious coordination

The scholarly record is increasingly digital and networked, while at the same time expanding in both the volume and diversity of the material it contains. The long-term future of the scholarly record cannot be effectively secured with traditional stewardship models developed for print materials. This report describes the key features of future stewardship models adapted to the characteristics of a digital, networked scholarly record, and discusses some practical implications of implementing these models. Key highlights include: As the scholarly record continues to evolve, conscious coordination will become an important organizing principle for stewardship models. Past stewardship models were built on an "invisible hand" approach that relied on the uncoordinated, institution-scale efforts of individual academic libraries acting autonomously to maintain local collections. Future stewardship of the evolving scholarly record requires conscious coordination of context, commitments, specialization, and reciprocity. With conscious coordination, local stewardship efforts leverage scale by collecting more of less. Keys to conscious coordination include right-scaling consolidation, cooperation, and community mix. Reducing transaction costs and building trust facilitate conscious coordination. Incentives to participate in cooperative stewardship activities should be linked to broader institutional priorities. The long-term future of the scholarly record in its fullest expression cannot be effectively secured with stewardship strategies designed for print materials. The features of the evolving scholarly record suggest that traditional stewardship strategies, built on an “invisible hand” approach that relies on the uncoordinated, institution-scale efforts of individual academic libraries acting autonomously to maintain local collections, is no longer suitable for collecting, organizing, making available, and preserving the outputs of scholarly inquiry. As the scholarly record continues to evolve, conscious coordination will become an important organizing principle for stewardship models. Conscious coordination calls for stewardship strategies that incorporate a broader awareness of the system-wide stewardship context; declarations of explicit commitments around portions of the local collection; formal divisions of labor within cooperative arrangements; and robust networks for reciprocal access. Stewardship strategies based on conscious coordination involve an acceleration of an already perceptible transition away from relatively autonomous local collections to ones built on networks of cooperation across many organizations, within and outside the traditional cultural heritage community

Desire Lines: Open Educational Collections, Memory and the Social Machine

This paper delineates the initial ideas around the development of the Co-Curate North East project. The idea of computerised machines which have a social use and impact was central to the development of the project. The project was designed with and for schools and communities as a digital platform which would collect and aggregate ‘memory’ resources and collections around local area studies and social identity. It was a co-curation process supported by museums and curators which was about the ‘meshwork’ between ‘official’ and ‘unofficial’ archives and collections and the ways in which materials generated from within the schools and community groups could themselves be re-narrated and exhibited online as part of self-organised learning experiences. This paper looks at initial ideas of social machines and the ways in machines can be used in identity and memory studies. It examines ideas of navigation and visualisation of data and concludes with some initial findings from the early stages of the project about the potential for machines and educational work

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

Supporting emerging researchers in data management and curation

While scholarly publishing remains the key means for determining researchers’ impact, international funding body requirements and government recommendations relating to research data management (RDM), sharing and preservation mean that the underlying research data are becoming increasingly valuable in their own right. This is true not only for researchers in the sciences but also in the humanities and creative arts as well. The ability to exploit their own - and others’ - data is emerging as a crucial skill for researchers across all disciplines. However, despite Generation Y researchers being ‘highly competent and ubiquitous users of information technologies generally’ they appears to be a widespread lack of understanding and uncertainty about open access and self-archived resources (Jisc study, 2012). This chapter will consider the potential support that academic librarians might provide to support Generation Y researchers in this shifting research data landscape and examine the role of the library as part of institutional infrastructure. The changing landscape will impact research libraries most keenly over the next few years as they work to develop infrastructure and support systems to identify and maintain access to a diverse array of research data outputs. However, the data that are being produced through research are no different to those being produced by artists, politicians and the general public. In this respect, all libraries - whether they be academic, national, or local - will need to be gearing up to ensure they are able to accept and provide access to an ever increasing range of complex digital objects

Establishing Incentives and Changing Cultures to Support Data Access

This project was developed as a key component of the workplan of the Expert Advisory Group on Data Access (EAGDA).EAGDA wished to understand the factors that help and hinder individual researchers in making their data (both published and unpublished) available to other researchers, and to examine the potential need for new types of incentives to enable data access and sharing. This is a critical challenge in achieving the shared policy commitment of the four EAGDA funders to maximise the benefit derived from data outputs and the considerable investment they have made over recent years in supporting data sharing.In addition to a review of previous reports and other initiatives in this area, the work involved in-depth interviews with key stakeholders; two focus group discussions; and a web survey to which 35 responses were received from a broad range of researchers and data managers.Although based on a relatively modest number of responses and interviews, the findings closely mirrored those of previous work in this area. In particular there was a clear, overarching view that the research culture and environment is not perceived as providing sufficient support, nor adequate rewards for researchers who generate and share high-quality datasets