"Just-in-time" generation of datasets by considering structured representations of given consent for GDPR compliance.

Data processing is increasingly becoming the subject of various policies and regulations, such as the European General Data Protection Regulation (GDPR) that came into effect in May 2018. One important aspect of GDPR is informed consent, which captures one's permission for using one's personal information for specific data processing purposes. Organizations must demonstrate that they comply with these policies. The fines that come with non-compliance are of such importance that it has driven research in facilitating compliance verification. The state-of-the-art primarily focuses on, for instance, the analysis of prescriptive models and posthoc analysis on logs to check whether data processing is compliant to GDPR. We argue that GDPR compliance can be facilitated by ensuring datasets used in processing activities are compliant with consent from the very start. The problem addressed in this paper is how we can generate datasets that comply with given consent "just-in-time". We propose RDF and OWL ontologies to represent the consent that an organization has collected and its relationship with data processing purposes. We use this ontology to annotate schemas, allowing us to generate declarative mappings that transform (relational) data into RDF driven by the annotations. We furthermore demonstrate how we can create compliant datasets by altering the results of the mapping. The use of RDF and OWL allows us to implement the entire process in a declarative manner using SPARQL. We have integrated all components in a service that furthermore captures provenance information for each step, further contributing to the transparency that is needed towards facilitating compliance verification. We demonstrate the approach with a synthetic dataset simulating users (re-)giving, withdrawing, and rejecting their consent on data processing purposes of systems. In summary, it is argued that the approach facilitates transparency and compliance verification from the start, reducing the need for posthoc compliance analysis common in the state-of-the-art.

Linearization of dose-response curve of the radiochromic film dosimetry system.

PURPOSE: Despite numerous advantages of radiochromic film dosimeter (high spatial resolution, near tissue equivalence, low energy dependence) to measure a relative dose distribution with film, one needs to first measure an absolute dose (following previously established reference dosimetry protocol) and then convert measured absolute dose values into relative doses. In this work, we present result of our efforts to obtain a functional form that would linearize the inherently nonlinear dose-response curve of the radiochromic film dosimetry system. METHODS: Functional form [ζ = (-1)[middle dot]netOD((2∕3))∕ln(netOD)] was derived from calibration curves of various previously established radiochromic film dosimetry systems. In order to test the invariance of the proposed functional form with respect to the film model used we tested it with three different GAFCHROMIC™ film models (EBT, EBT2, and EBT3) irradiated to various doses and scanned on a same scanner. For one of the film models (EBT2), we tested the invariance of the functional form to the scanner model used by scanning irradiated film pieces with three different flatbed scanner models (Epson V700, 1680, and 10000XL). To test our hypothesis that the proposed functional argument linearizes the response of the radiochromic film dosimetry system, verification tests have been performed in clinical applications: percent depth dose measurements, IMRT quality assurance (QA), and brachytherapy QA. RESULTS: Obtained R(2) values indicate that the choice of the functional form of the new argument appropriately linearizes the dose response of the radiochromic film dosimetry system we used. The linear behavior was insensitive to both film model and flatbed scanner model used. Measured PDD values using the green channel response of the GAFCHROMIC™ EBT3 film model are well within ±2% window of the local relative dose value when compared to the tabulated Cobalt-60 data. It was also found that criteria of 3%∕3 mm for an IMRT QA plan and 3%∕2 mm for a brachytherapy QA plan are passing 95% gamma function points. CONCLUSIONS: In this paper, we demonstrate the use of functional argument to linearize the inherently nonlinear response of a radiochromic film based reference dosimetry system. In this way, relative dosimetry can be conveniently performed using radiochromic film dosimetry system without the need of establishing calibration curve.

Ethics and diversity in artificial intelligence policies, strategies and initiatives.

A burgeoning of Artificial Intelligence (AI) technologies in recent years has led to increased discussion about its potential to address many issues considered otherwise intractable, including those highlighted by the United Nations 2030 Agenda for Sustainable Development and associated Sustainable Development Goals. In tandem with this growth in AI is an expanding body of documentation regarding how such advanced technologies should be governed and managed. Issued by a variety of sources and comprising frameworks, policies and guidelines, this body of work encompasses the legal, social, ethical and policy issues around AI. With at least 470 such documents identified, as of May 2021, in the Council of Europe's tracker of AI initiatives, questions are emerging around the diversity of views expressed, especially regarding the influence of the Global North or Euro-American perspectives. Our previous analysis of a corpus of largely grey literature discovered blind spots regarding both gender representation and perspectives from the Global South. Expanding on that work, this paper examines a significantly extended corpus, with a focus on the role of underrepresented groups in the wider AI discourse. We find that voices from the Global South and consideration of alternative ethical approaches are largely absent from the conversation. In light of the prominence of social, cultural and ethical perspectives from the Global North, this paper explores implications for the development of standards for ethical AI. Concluding by offering approaches to incorporate more diverse ethical viewpoints and beliefs, we call for increased consideration of power structures when developing AI ethics policies and standards within these alternative socio-cultural and socio-economic contexts.