AI-Related Risk: An Epistemological Approach

Abstract

Risks connected with AI systems have become a recurrent topic in public and academic debates, and the European proposal for the AI Act explicitly adopts a risk-based tiered approach that associates different levels of regulation with different levels of risk. However, a comprehensive and general framework to think about AI-related risk is still lacking. In this work, we aim to provide an epistemological analysis of such risk building upon the existing literature on disaster risk analysis and reduction. We show how a multi-component analysis of risk, that distinguishes between the dimensions of hazard, exposure, and vulnerability, allows us to better understand the sources of AI-related risks and effectively intervene to mitigate them. This multi-component analysis also turns out to be particularly useful in the case of general-purpose and experimental AI systems, for which it is often hard to perform both ex-ante and ex-post risk analyses.

The Fourth Industrial Revolution: Its Impact on Artificial Intelligence and Medicine in Developing Countries

Abstract

Artificial intelligence (AI) is the ability of a digital computer or computer-controlled robot to perform tasks commonly associated with intelligent beings. Artificial intelligence can be both a blessing and a curse, and potentially a double-edged sword if not carefully wielded. While it holds massive potential benefits to humans—particularly in healthcare by assisting in treatment of diseases, surgeries, record keeping, and easing the lives of both patients and doctors, its misuse has potential for harm through impact of biases, unemployment, breaches of privacy, and lack of accountability to mention a few. In this article, we discuss the fourth industrial revolution, through a focus on the core of this phenomenon, artificial intelligence. We outline what the fourth industrial revolution is, its basis around AI, and how this infiltrates human lives and society, akin to a transcendence. We focus on the potential dangers of AI and the ethical concerns it brings about particularly in developing countries in general and conflict zones in particular, and we offer potential solutions to such dangers. While we acknowledge the importance and potential of AI, we also call for cautious reservations before plunging straight into the exciting world of the future, one which we long have heard of only in science fiction movies.

Multispecies deep learning using citizen science data produces more informative plant community models

Abstract

In the age of big data, scientific progress is fundamentally limited by our capacity to extract critical information. Here, we map fine-grained spatiotemporal distributions for thousands of species, using deep neural networks (DNNs) and ubiquitous citizen science data. Based on 6.7 M observations, we jointly model the distributions of 2477 plant species and species aggregates across Switzerland with an ensemble of DNNs built with different cost functions. We find that, compared to commonly-used approaches, multispecies DNNs predict species distributions and especially community composition more accurately. Moreover, their design allows investigation of understudied aspects of ecology. Including seasonal variations of observation probability explicitly allows approximating flowering phenology; reweighting predictions to mirror cover-abundance allows mapping potentially canopy-dominant tree species nationwide; and projecting DNNs into the future allows assessing how distributions, phenology, and dominance may change. Given their skill and their versatility, multispecies DNNs can refine our understanding of the distribution of plants and well-sampled taxa in general.

Modeling deficit irrigation water demand of maize and potato in Eastern Germany using ERA5-Land reanalysis climate time series

Abstract

ERA5-Land reanalysis (ELR) climate time series has proven useful in (hydro)meteorological studies, however, its adoption for local studies is limited due to accuracies constraints. Meanwhile, local agricultural use of ELR could help data-scarce countries by addressing gaps in (hydro)meteorological variables. This study aimed to evaluate the first applicability of the ELR climate time series for modeling maize and potato irrigation water demand (IWD) at field scale and examined the performance of ELR precipitation with bias correction (DBC) and without bias correction (WBC). Yield, actual evapotranspiration (ETa), irrigation, water balance, and crop water productivity (CWP) were evaluated using the deficit irrigation toolbox. The study found that maize (13.98–14.49 ton/ha) and potato (6.84–8.20 tons/ha) had similar mean seasonal yield under different irrigation management strategies (IMS). The Global Evolutionary Technique for OPTimal Irrigation Scheduling (GET-OPTIS_WS) IMS had the highest mean seasonal yields under DBC and WBC, while rainfall and constant IMS had the most crop failures. DBC had a higher mean seasonal ETa than WBC, except for the potato FIT and rainfall IMS. Global Evolutionary Technique for OPTimal Irrigation Scheduling: one common schedule per crop season (GET-OPTIS_OS) and GET-OPTIS_WS IMS outperformed conventional IMS in IWD by 44%. Overall, GET-OPTIS_OS and GET-OPTIS_WS performed best for maize and potato CWP in terms of IWD, scheduling, and timing. Therefore, adoption of ELR climate time series and advanced irrigation optimization strategies such as GET-OPTIS_OS and GET-OPTIS_WS can be beneficial for effective and efficient management of limited water resources, where agricultural water allocation/resource is limited.

Epistemic Trust in Scientific Experts: A Moral Dimension

Abstract

In this paper, I develop and defend a moralized conception of epistemic trust in science against a particular kind of non-moral account defended by John (2015, 2018). I suggest that non-epistemic value considerations, non-epistemic norms of communication and affective trust properly characterize the relationship of epistemic trust between scientific experts and non-experts. I argue that it is through a moralized account of epistemic trust in science that we can make sense of the deep-seated moral undertones that are often at play when non-experts (dis)trust science.

Public Health Expenditure and Health Outcomes in Africa: The Moderating Effect of ICT Development

Abstract

This study investigates whether ICT development modulates the effect of public health expenditure on health outcomes in a sample of forty-eight (48) African countries over the period 2000–2018. We approach health outcomes through under-five mortality, infant mortality, and life expectancy at birth. As measures of ICT indicators, we use mobile cellular subscriptions, the number of Internet users and an ICT development index constructed through principal component analysis (PCA) on the two previous ICT indicators. In addition, the relationship between public health expenditure, ICT development and health outcomes is examined in a dynamic framework using the system generalized method of moments (System-GMM). The results show that ICT development is important in the relationship between public health expenditure and health outcomes in Africa. Indeed, the net effect obtained when ICT is associated with public health expenditure is negative for mortality variables and positive for life expectancy at birth. These results suggest that the penetration of ICT in the African health system improves the population health. The policy implication is that African policymakers should promote the ICT diffusion in the health sector so that the goal of significantly improving health outcomes through committed investments in this sector is achieved in a sustainable manner in Africa.

Typical and extreme weather datasets for studying the resilience of buildings to climate change and heatwaves

Abstract

We present unprecedented datasets of current and future projected weather files for building simulations in 15 major cities distributed across 10 climate zones worldwide. The datasets include ambient air temperature, relative humidity, atmospheric pressure, direct and diffuse solar irradiance, and wind speed at hourly resolution, which are essential climate elements needed to undertake building simulations. The datasets contain typical and extreme weather years in the EnergyPlus weather file (EPW) format and multiyear projections in comma-separated value (CSV) format for three periods: historical (2001–2020), future mid-term (2041–2060), and future long-term (2081–2100). The datasets were generated from projections of one regional climate model, which were bias-corrected using multiyear observational data for each city. The methodology used makes the datasets among the first to incorporate complex changes in the future climate for the frequency, duration, and magnitude of extreme temperatures. These datasets, created within the IEA EBC Annex 80 “Resilient Cooling for Buildings”, are ready to be used for different types of building adaptation and resilience studies to climate change and heatwaves.