Sharing is CAIRing: Characterizing Principles and Assessing Properties of Universal Privacy Evaluation for Synthetic Tabular Data

Data sharing is a necessity for innovative progress in many domains, especially in healthcare. However, the ability to share data is hindered by regulations protecting the privacy of natural persons. Synthetic tabular data provide a promising solution to address data sharing difficulties but does not inherently guarantee privacy. Still, there is a lack of agreement on appropriate methods for assessing the privacy-preserving capabilities of synthetic data, making it difficult to compare results across studies. To the best of our knowledge, this is the first work to identify properties that constitute good universal privacy evaluation metrics for synthetic tabular data. The goal of such metrics is to enable comparability across studies and to allow non-technical stakeholders to understand how privacy is protected. We identify four principles for the assessment of metrics: Comparability, Applicability, Interpretability, and Representativeness (CAIR). To quantify and rank the degree to which evaluation metrics conform to the CAIR principles, we design a rubric using a scale of 1-4. Each of the four properties is scored on four parameters, yielding 16 total dimensions. We study the applicability and usefulness of the CAIR principles and rubric by assessing a selection of metrics popular in other studies. The results provide granular insights into the strengths and weaknesses of existing metrics that not only rank the metrics but highlight areas of potential improvements. We expect that the CAIR principles will foster agreement among researchers and organizations on which universal privacy evaluation metrics are appropriate for synthetic tabular data

Danish Companies Dashboard: An Interactive, Geospatial Visualisation of Industries and Profit in Denmark

Profound knowledge of the business landscape is crucial for any company wanting to affect its position in the market. Whereas corresponding data is publicly available, visual interfaces that inform on the distribution of companies operating in different sectors are rare. To close the gap for the Danish market, we developed the Danish Company Dashboard (DCD), which uses the Danish Business Authority’s database on company data to visually explore how the different companies, grouped by industries, are geographically scattered across Denmark on a regional and municipality plane. Moreover, the study and the accompanying visualisations provide insights into how the profit of each industry and company differs throughout the regions and municipalities, thereby supporting strategic decision making tasks of industry stakeholders

Danish Companies Dashboard: An Interactive, Geospatial Visualisation of Industries and Profit in Denmark

Profound knowledge of the business landscape is crucial for any company wanting to affect its position in the market. Whereas corresponding data is publicly available, visual interfaces that inform on the distribution of companies operating in different sectors are rare. To close the gap for the Danish market, we developed the Danish Company Dashboard (DCD), which uses the Danish Business Authority’s database on company data to visually explore how the different companies, grouped by industries, are geographically scattered across Denmark on a regional and municipality plane. Moreover, the study and the accompanying visualisations provide insights into how the profit of each industry and company differs throughout the regions and municipalities, thereby supporting strategic decision making tasks of industry stakeholders

Danish Companies Dashboard: An Interactive, Geospatial Visualisation of Industries and Profit in Denmark

Profound knowledge of the business landscape is crucial for any company wanting to affect its position in the market. Whereas corresponding data is publicly available, visual interfaces that inform on the distribution of companies operating in different sectors are rare. To close the gap for the Danish market, we developed the Danish Company Dashboard (DCD), which uses the Danish Business Authority’s database on company data to visually explore how the different companies, grouped by industries, are geographically scattered across Denmark on a regional and municipality plane. Moreover, the study and the accompanying visualisations provide insights into how the profit of each industry and company differs throughout the regions and municipalities, thereby supporting strategic decision making tasks of industry stakeholders

Heart-to-heart with ChatGPT: the impact of patients consulting AI for cardiovascular health advice

Objectives The advent of conversational artificial intelligence (AI) systems employing large language models such as ChatGPT has sparked public, professional and academic debates on the capabilities of such technologies. This mixed-methods study sets out to review and systematically explore the capabilities of ChatGPT to adequately provide health advice to patients when prompted regarding four topics from the field of cardiovascular diseases.Methods As of 30 May 2023, 528 items on PubMed contained the term ChatGPT in their title and/or abstract, with 258 being classified as journal articles and included in our thematic state-of-the-art review. For the experimental part, we systematically developed and assessed 123 prompts across the four topics based on three classes of users and two languages. Medical and communications experts scored ChatGPT’s responses according to the 4Cs of language model evaluation proposed in this article: correct, concise, comprehensive and comprehensible.Results The articles reviewed were fairly evenly distributed across discussing how ChatGPT could be used for medical publishing, in clinical practice and for education of medical personnel and/or patients. Quantitatively and qualitatively assessing the capability of ChatGPT on the 123 prompts demonstrated that, while the responses generally received above-average scores, they occupy a spectrum from the concise and correct via the absurd to what only can be described as hazardously incorrect and incomplete. Prompts formulated at higher levels of health literacy generally yielded higher-quality answers. Counterintuitively, responses in a lower-resource language were often of higher quality.Conclusions The results emphasise the relationship between prompt and response quality and hint at potentially concerning futures in personalised medicine. The widespread use of large language models for health advice might amplify existing health inequalities and will increase the pressure on healthcare systems by providing easy access to many seemingly likely differential diagnoses and recommendations for seeing a doctor for even harmless ailments