Both because of the shortcomings of existing risk assessment methodologies, as well as newly available tools to predict hazard and risk with machine learning approaches, there has been an emerging emphasis on probabilistic risk assessment. Increasingly sophisticated AI models can be applied to a plethora of exposure and hazard data to obtain not only predictions for particular endpoints but also to estimate the uncertainty of the risk assessment outcome. This provides the basis for a shift from deterministic to more probabilistic approaches but comes at the cost of an increased complexity of the process as it requires more resources and human expertise. There are still challenges to overcome before a probabilistic paradigm is fully embraced by regulators. Based on an earlier white paper (Maertens et al., 2022), a workshop discussed the prospects, challenges and path forward for implementing such AI-based probabilistic hazard assessment. Moving forward, we will see the transition from categorized into probabilistic and dose-dependent hazard outcomes, the application of internal thresholds of toxicological concern for data-poor substances, the acknowledgement of user-friendly open-source software, a rise in the expertise of toxicologists required to understand and interpret artificial intelligence models, and the honest communication of uncertainty in risk assessment to the public.
Abstract: Since the first Oxford Survey of Childhood Cancer’s results were published, people have become more aware of the risks associated with prenatal exposure from diagnostic x rays. As a result, it has since been the subject of many studies. In this review, the results of recent epidemiological studies are summarized. The current international guidelines for diagnostic x-ray examinations were compared to the review. All epidemiological studies starting from 2007 and all relevant international guidelines were included. Apart from one study that involved rhabdomyosarcoma, no statistically significant associations were found between prenatal exposure to x rays and the development of cancer during 2007–2020. Most of the studies were constrained in their design due to too small a cohort or number of cases, minimal x-ray exposure, and/or data obtained from the exposed mothers instead of medical reports. In one of the studies, computed tomography exposure was also included, and this requires more and longer follow-up in successive studies. Most international guidelines are comparable, provide risk coefficients that are quite conservative, and discourage abdominal examinations of pregnant women.
There is an urgency and need to develop an innovative strategic approach for organizations to develop a sustainable organization for the future, in which they are able to respond resiliently to major environmental challenges and changes in the short term and adjust the management of the organization. On the same time, in this strategic approach learning and transforming accordingly in the long term is involved as well. This approach will give organizations the opportunity to operationalize their boards’ and stakeholders’ ambitions to build a responsible business, with focus on governance elements, as well as interaction with social and environmental factors, risk, and strategy from a holistic view. In education, students could work with this approach in future projects for real companies.
The goal of UPIN is to develop and evaluate a scalable distributed system that enables users to cryptographically verify and easily control the paths through which their data travels through an inter-domain network like the Internet, both in terms of router-to-router hops as well as in terms of router attributes (e.g., their location, operator, security level, and manufacturer). UPIN will thus provide the solution to a very relevant and current problem, namely that it is becoming increasingly opaque for users on the Internet who processes their data (e.g., in terms of service providers their data passes through as well as what jurisdictions apply) and that they have no control over how it is being routed. This is a risk for people’s privacy (e.g., a malicious network compromising a user’s data) as well as for their safety (e.g., an untrusted network disrupting a remote surgery). Motivating examples in which (sensitive) user data typically travels across the Internet without user awareness or control are: - Internet of Things for consumers: sensors such as sleep trackers and light switches that collect information about a user’s physical environment and send it across the Internet to remote services for analysis. - Medical records: health care providers requiring medical information (e.g., health records of patients or remote surgery telemetry) to travel between medical institutions according to specified agreements. - Intelligent transport systems: communication plays a crucial role in future autonomous transportation systems, for instance to avoid freight drones colliding or to ensure smooth passing of trucks through busy urban areas. The UPIN project is novel in three ways: 1. UPIN gives users the ability to control and verify the path that their data takes through the network all the way to the destination endpoint, both in terms of hops and attributes of routers traversed. UPIN accomplishes this by adding and improving remote attestation techniques for on-path routers to existing path verification mechanisms, and by adopting and further developing in-packet path selection directives for control. 2. We develop and simulate data and control plane protocols and router extensions to include the UPIN system in inter-domain networking systems such as IP (e.g., using BGP and segment routing) and emerging systems such as SCION and RINA. 3. We evaluate the scalability and performance of the UPIN system using a multi-site testbed of open programmable P4 routers, which is necessary because UPIN requires novel packet processing functions in the data plane. We validate the system using the earlier motivating examples as use cases. The impact we target is: - Increased trust from users (individuals and organizations) in network services because they are able to verify how their data travels through the network to the destination endpoint and because the UPIN APIs enable novel applications that use these network functions. - More empowered users because they are able to control how their data travels through inter-domain networks, which increases self-determination, both at the level of individual users as well as at the societal level.
