Rethinking the Integration of Artificial Intelligence Into Surgery: Centralized Risk Assessment for System-Level Impact

The demand for integrated modelling of different environmental compartments is increasing. With steadily growing computational power it seems possible to overcome the obstacles still preventing the comprehensive numerical treatment of the environmental system including society and economy. This article deals with approaches to the combined treatment of air, water and soil pollution aiming at impact and risk assessment. The design of forward and inverse (adjoint) model systems is discussed and examples of applications focussing on air pollution impacts are given. It is evident that complex system applications to risk and impact assessment are still at their beginning and that there is still considerable potential to be exploited.

Effective volcanic impact and risk assessment underpins effective volcanic disaster risk management. Yet contemporary volcanic risk assessments face a number of challenges, including delineating hazard and impact sequences, and identifying and quantifying systemic risks. A more holistic approach to impact assessment is required, which incorporates the complex, multi-hazard nature of volcanic eruptions and the dynamic nature of vulnerability before, during and after a volcanic event. Addressing this need requires a multidisciplinary, integrated approach, involving scientists and stakeholders to co-develop decision-support tools that are scientifically credible and operationally relevant to provide a foundation for robust, evidence-based risk reduction decisions. This study presents a dynamic, longitudinal impact assessment framework for multi-phase, multi-hazard volcanic events and applies the framework to interdependent critical infrastructure networks in the Taranaki region of Aotearoa New Zealand, where Taranaki Mounga volcano has a high likelihood of producing a multi-phase explosive eruption within the next 50 years. In the framework, multi-phase scenarios temporally alternate multi-hazard footprints with risk reduction opportunities. Thus, direct and cascading impacts and any risk management actions carry through to the next phase of activity. The framework forms a testbed for more targeted mitigation and response planning and allows the investigation of optimal intervention timing for mitigation strategies during an evolving eruption. Using ‘risk management’ scenarios, we find the timing of mitigation intervention to be crucial in reducing disaster losses associated with volcanic activity. This is particularly apparent in indirect, systemic losses that cascade from direct damage to infrastructure assets. This novel, dynamic impact assessment approach addresses the increasing end-user need for impact-based decision-support tools that inform robust response and resilience planning.

As water management is still a problem of international concern, scientists and practitioners are collaborating to develop new tools and methods to improve and help in the decision-making process. When addressing the priority pollutant monitoring and impact assessment, the ecotoxicity effects, carcinogenic and non-carcinogenic, should be considered together with the exposure factor and health hazards. The main goals of this study were to assess the ecological and health hazards and to apply integrated impact and risk assessment based on the ecotoxicity and exposure factors of each priority pollutant present in the aquatic ecosystem. This study used as a database the measured concentrations of 5 inorganic and 14 organic priority pollutants from the Siret river basin from NE Romania, from 18 river sections monitored in the period 2015–2020. The USEtox methodology and a new integrated index for environmental impact and risk assessment were developed and applied to evaluate the ecological and health hazards and environmental impacts and risks within the river basin. The total impact scores for heavy metals ranged from 2 × 103 to 2.25 × 109, and those for organic pollutants ranged from 2.72 × 10−1 to 2.95 × 106. The environmental risk in the case of inorganic priority pollutants ranged between 5.56 and 3136.35, and that in the case of organic pollutants was between 4.69 and 4059.17. The results revealed that there is a major to catastrophic environmental impact in almost all monitored river sections (10 out of 18), and the overall risk exposure was found to be at a significant to a major level. This study proved the harmful effects that the priority pollutants may have, even in very small concentrations, on non-target organisms and suggests that greater control over the pollution sources and mitigation of environmental impacts and risks should be applied.

Employing seasonal forecasting in the domain of impact and risk assessment is particularly beneficial. It facilitates early warning systems and proactive adaptation strategies, which are essential for minimizing the adverse effects of heat waves. This proactive approach is crucial for public health, urban planning, and disaster management, where timely information can significantly alter response strategies and mitigation measures. This study, we underscore the value of integrating high-quality climate data with impact assessment models. It demonstrates how bridging the gap between climate science and practical risk management can lead to more effective and informed decision-making processes in the face of climate change challenges. In our study, we integrate Copernicus seasonal forecasting data with the CLIMADA platform, adopting a forward-looking approach to assess the potential impacts of heat waves on populations. This integration involved developing a bespoke pipeline to seamlessly bridge the gap between raw forecasting data from Copernicus and the analytical capabilities of CLIMADA, an ETH Zurich-developed tool for climate impact and risk assessment. The focus is not only on facilitating data integration but also on automating the processing and communication of results. One significant aspect of this work is managing extensive datasets containing multiple simulations. To efficiently handle this, we implemented an automated system for data extraction, transformation, and loading. This is crucial in maintaining the integrity and usability of the data within CLIMADA's impact modeling framework. Part of this process also entailed resolving spatial and temporal alignment issues, a step essential to ensuring the ability of the seasonal forecasting data to reflect the potential heat wave impacts. Our approach aim to streamline the complexities of large-scale climate data, enhancing the precision and effectiveness of our assessments. Building a pipeline that links these probabilistic forecasting with impact assessment tools has multiple benefits. It enhances the capability to identify critical data needs and model improvements, thus fostering a feedback loop that drives data and model refinement. Furthermore, it contributes to laying the groundwork for the effective use of the next generation of seasonal forecast data, potentially transforming how we prepare for and adapt to climate risks.

Adapting social impact assessment to address a project's human rights impacts and risks

ChatGPT and the future of impact assessment

Over 150 forms of impact assessment can be identified using Google searches, with several new forms appearing since 2003. Since then, the popularity of the various members of the impact assessment family has changed, partly in response to legislative and regulatory changes, and general trends in society. The information explosion and expansion of the internet has resulted in a 32 fold increase in the number of hits for "impact assessment", now over 12 million. The conventional methods most frequently mentioned in 2003 had relatively low proportional change over the last 10 years but remain amongst the most frequently mentioned in 2014: risk assessment, public participation, cost-benefit analysis, public involvement, environmental monitoring, and project evaluation. The terms with highest proportional change (i.e. the super-hot topics) were primarily social concerns, including: equality impact assessment, welfare impact assessment, mental health impact assessment, disability impact assessment, human impact assessment, social impact assessment, and social risk assessment. Other terms that had high proportional change included life cycle impact assessment. Information about the relative popularity of the various forms of impact assessment is used in this paper to discuss issues and trends in the broad field of impact assessment.

In this article an impact assessment is carried out, generated by activities in the energy sector related to Rovinari mining area. Energy-related activities in this area are carried out by open pit mining activities in the context of Tismana, Roșia, Pinoasa, Peşteana and Rovinari locations and Rovinari thermal power plant. Some information that has formed the input for the author’s approach has been provided by SC. Complexul Energetic Oltenia SA (CEO) and S.C. Institute of Scientific Research, Technological Engineering and Mine Designs on Lignite S.A. Craiova (ICSITPML) which has been processed, analysed and used for the presented environmental impact assessment. Two methods from specialized studies have been used for the impact assessment, namely: the Matrix Method for Rapid Impact Assessment (MERI) and the Method for Integrated Quantitative Impact and Risk Assessment of Environmental Pollution (EIRM). Based on the analysis carried out, it can be concluded that the activity in this sector does not lead to significant negative effects strictly associated with it.

Robust impact assessments (IAs) for deep-sea fisheries are essential for safeguarding deep-sea ecosystems against the impacts of bottom fishing. In the high seas, United Nations Resolution commitments require States (independently or through Regional Fisheries Management Organisations (RFMOs)) to conduct IAs to evaluate if fishing is putting vulnerable marine ecosystems (VMEs) at risk. To enhance the efficacy of future IAs, this study evaluated nine IAs against the criteria in the FAO International Guidelines for the Management of Deep-Sea Fisheries in the High Seas. We find that in all IAs, the information required by the FAO Guidelines is either completely lacking or only partially addressed. The main shortcoming of the IAs was inadequate description of the ecosystems potentially affected by fishing. Additional shortcomings include incomplete description of the proposed fishing activities, lack of baseline data and risk assessments, and limited consideration of the indirect impacts of fishing. This study identifies several ways to strengthen the IA process; i) making IAs publicly available; ii) improved collection of baseline data and VME identification; iii) assessment of impacts on broader range of species associated with VMEs or potentially impacted by deep-sea fishing; iv) enhanced cooperation between RFMOs and more streamlined IA processes; v) comprehensive assessment of different types of impacts from fishing and climate change, and vii) improved consideration of uncertainty. Fully compliant IAs are a minimum requirement for the effective function of RFMOs, since they are the primary tool for preventing significant adverse impacts upon vulnerable marine ecosystems and the wider deep-sea environment.

Impact assessments are increasingly employed and debated as instruments for mitigating the fundamental rights risks associated with artificial intelligence, platforms and personal data processing. However, before their adoption in connection with technology and fundamental rights, impact assessments have been used for decades to mitigate large undertakings’ environmental and social impacts. An impact assessment is a process for collecting information to identify a future action’s effects and mitigate its unwanted effects. This article proposes that impact assessments represent a distinct legal design pattern with core elements that can be replicated in new legal contexts requiring ex-ante identification and mitigation of foreseeable risks. The tensions between diverging interests, temporality, epistemics and economics characterise this legal design pattern. The impact assessment process seeks to resolve these tensions by enabling translation between the regulator, the executor of the planned action and the stakeholders impacted by it. Awareness of the underlying patterns allows the lawmaker or the regulator to learn across diverse impact assessment models. Design pattern thinking advances research both on law and regulation by uncovering the tensions underling the design solution, as well as pattern interaction between legally mandated impact assessments and those representing other regulatory instruments. Finally, the approach raises awareness of the instrument’s shortcomings, including spheres where relying on complementary legal design patterns, such as precautionary principle, is more justified.

The success of AI solutions in health systems depends on governance from use case inception through deployment and auditing. This proposed early pipeline governance framework for vendor AI solutions highlights a four-pronged approach: strategic alignment, executive sponsorship, impact and value case assessment, and risk assessment. Each component can be scaled to health systems of any size and the risk and impact assessments can take place simultaneously or sequentially.

The Framework Act on Administration, scheduled to take effect in March 2021, provides an explicit basis for fully automated administrative decisions by introducing the concept of ‘automatic disposition’ in Article 20. Traditionally, the ‘automation of administration’ was significant as an auxiliary administrative procedure consisting of automatic mechanical devices such as traffic signal transmission, tax, and utility bill calculation. Moreover, since automated administrative decisions using artificial intelligence (AI) technology are also included in this automatic disposition, there is room for legal acceptance of automated administrative decisions based on artificial intelligence algorithms that exceed the level of conventional computerization or partial automation. However, it is still a long way from implementing a fully automated administrative decision system based on current law and having technical and institutional safety devices for it. On the other hand, the Framework Act on Intelligent Informatization, which took effect in December 2020, introduced a new ‘social impact assessment’ for intelligent information services that have far-reach effects on citizens’ lives. According to Article 56 of the Act, the main goal of this impact assessment system is to investigate and evaluate the impact of intelligent information services on society, economy, culture, etc., disclose the results, reflect them in policies, and directly recommend appropriate measures. Since the scope of intelligent information services subject to evaluation is not limited to the private sector, it should be considered that intelligent information services in the administrative sector can also be subject to social impact assessment in this article. It is reasonable to assume that automatic administrative services by artificial intelligence system belong to a wide range of intelligent information services prescribed by law, and that automatic disposition issued as a fully automated system based on intelligent information technology can also be subject to social impact assessment. Although no provisions have been found in law to assess the impact of automated administrative decisions, the government of Canada has implemented the “Directive on Automated Decision-Making” since April 2019, which systematically regulates the automated administrative decision-making system. The requirement that constitutes an important axis of this directive is the ‘algorithmic impact assessment'. For an automated administrative decision-making system that bears relatively stricter legality and public responsibility than private companies' services, it is necessary to process and operate an impact assessment that reviews safety, transparency, and solidity in advance. In this paper, referring to the Canadian case, as a control system of the algorithm-based administrative decision-making system, I propose the institutionalization of the “algorithmic impact assessment” in consideration of the connection with the social impact assessment under the current law.

In this contribution to IAPA’s special issue on artificial intelligence (AI), I ask how AI can enable IA (impact assessment) to contribute to sustainable development? The point I want to make is that AI in general (not the AI that directly supports IA) could undermine the trust society has in IA. If their goal is to contribute to sustainable development, IA practitioners should understand and promote the enabling conditions that support the trust society has in them. By analyzing how that trust can emerge, I look for such conditions, looking again for some clues on how AI could support these conditions. Spoiler alert: that is not easy.

Wastewaters from the chemical industry may contain a large number of both known/identified and unknown/not identified chemical substances, and the composition of the effluents may vary greatly from plant to plant. Usually only a minor fraction of the chemicals present are identified and adequate environmental property data may be available only for a small number of these. Effluent characterizations in terms of summary parameters are therefore necessary to complement chemical specific characterizations, but the selection of suitable summary parameters other than traditionally wastewater parameters depends on the particular effluent and the available information. The types of pollution effects to be mitigated depend on the intrinsic properties and volumes of effluent discharged as well as on the initial dilution and the specific characteristics and designated uses of the receiving water. For these reasons management decisions should be based on individual case specific environmental impact or risk assessments and simultaneous individual technical and economical evaluations of potential remedial actions (comprising cleaner technology options, wastewater treatment, and improved dilution in the receiving water). This paper presents a generalized integrated impact assessment and control strategy for marine discharges based upon Danish experiences over a 10 year period.

Many river deltas and coastal lowlands in the world are densely populated and located in the Global South. Due to relative sea-level rise (rSLR), they face an increasing risk of drowning and flooding and thus require reliable impact and risk assessments of rSLR and flooding. As both sea-level rise (SLR) impact and flood inundation are closely related to land elevation, the quality of these assessments largely relies on vertical accuracy and proper datum referencing of the elevation data used. However, high-quality digital elevation models (DEMs) representing elevation at spatial resolutions and vertical accuracies at centimetre to decimetre scale are still not available or accessible for major parts of the Earth’s coasts, including densely populated Asian and African coastal lowlands or Small Island Development States. In these regions, global DEMs are often used even though they suffer from large vertical errors and artefacts, thereby impacting the quality of flood exposure assessments. While the accuracy of those global DEMs is extensively addressed both in their dataset documentation and literature, the relevance and proper vertical datum conversion from global geoid and ellipsoid models to local sea level is often still omitted in many applied studies; in part because the process is complicated in data-poor regions, where tide gauge records are often insufficient or outdated. Sea surface data based on satellite altimetry may serve as a substitute but the referencing of the land elevation and sea surface data to a common vertical datum includes several steps of datum conversion beforehand, which – if not performed properly – can introduce local errors of sea level to the land elevation up to several metres. In this study, we test and present a workflow of globally consistent vertical datum conversion of elevation data to continuous local mean sea level by integrating globally available data on coastal elevation and sea surface. We apply our approach to recently published global DEMs and validate them for several key coastal lowlands such as the Ayeyarwady and Mekong Deltas, and show the improvement of the performance of global DEMs for impact assessments in data-poor regions. This proves the potential to improve impact assessments of SLR and flood exposure in coastal lowlands around the world where high-quality elevation information is not accessible.