48,199 research outputs found
Provenance documentation to enable explainable and trustworthy AI: A literature review
ABSTRACTRecently artificial intelligence (AI) and machine learning (ML) models have demonstrated remarkable progress with applications developed in various domains. It is also increasingly discussed that AI and ML models and applications should be transparent, explainable, and trustworthy. Accordingly, the field of Explainable AI (XAI) is expanding rapidly. XAI holds substantial promise for improving trust and transparency in AI-based systems by explaining how complex models such as the deep neural network (DNN) produces their outcomes. Moreover, many researchers and practitioners consider that using provenance to explain these complex models will help improve transparency in AI-based systems. In this paper, we conduct a systematic literature review of provenance, XAI, and trustworthy AI (TAI) to explain the fundamental concepts and illustrate the potential of using provenance as a medium to help accomplish explainability in AI-based systems. Moreover, we also discuss the patterns of recent developments in this area and offer a vision for research in the near future. We hope this literature review will serve as a starting point for scholars and practitioners interested in learning about essential components of provenance, XAI, and TAI
Users' trust in information resources in the Web environment: a status report
This study has three aims; to provide an overview of the ways in which trust is either assessed or asserted in relation to the use and provision of resources in the Web environment for research and learning; to assess what solutions might be worth further investigation and whether establishing ways to assert trust in academic information resources could assist the development of information literacy; to help increase understanding of how perceptions of trust influence the behaviour of information users
Recent advances in the application of stable isotope ratio analysis in forensic chemistry
This review paper updates the previous literature in relation to the continued and developing use of stable isotope ratio analysis in samples which are relevant to forensic science. Recent advances in the analysis of drug samples, explosive materials, and samples derived from human and animal samples are discussed. The paper also aims to put the use of isotope ratio mass spectrometry into a forensic context and discuss its evidential potential
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
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