for scientific, peer-reviewed literature.This achievement is a major milestone: increased visibility, global reach, confirmation of quality and compliance with funding conditions for authors.
Modeling for policy has become an integral part of policy making and technology assessment. This became particularly evident to the general public when, during the COVID-19 pandemic, forecasts of infection dynamics based on computer simulations were used to evaluate and justify policy containment measures. Computer models are also playing an increasing role in technology assessment (TA). Computer simulations are used to explore possible futures related to specific technologies, for example, in the area of energy systems analysis. Artificial intelligence (AI) models are also becoming increasingly important. The results is a mix of methods where computer simulations and machine learning converge, posing particular challenges and opening up new research questions. This Special topic brings together case studies from different fields to explore the current state of computational models in general and AI methods in particular for policy and TA.
Three years ago, the sudden onset of the COVID-19 pandemic challenged academia just like any other societal field, while at the same time putting science center stage. Media attention tended to focus on particular disciplines, such as epidemiology and microbiology, and on individual, mostly local, experts. Based on the idea that science as a global, multidisciplinary community has something to offer society beyond the highly specialized output of individual research fields prepared for local, short-term perspectives, the Institute of Technology Assessment of the Austrian Academy of Sciences launched a spontaneous expert survey in June 2021 with a global and interdisciplinary aspiration, addressing three non-standard issues related to the pandemic and its management: side effects, opportunities, and preparedness. In this paper, we present our methodology and the results of our analysis. We conclude with a discussion of potential contributions of technology assessment in times of sudden, global crises.
In nano risk governance, we observe a trend toward coupling and integrating a variety of computational models into integrated risk governance tools. This article discusses the development and design of such integrated tools as ‘nano risk governance imaginaries in the making.’ Using an illustrative example, the SUNDS tool, we show how the tool manifests conceptual shifts from risk to innovation governance, a technocratic evidence culture based on the quantification of risks, and an envisioned application in industrial innovation management. This conceptualization runs the risk of narrowing the view of nano risks and cementing the widely lamented democratic deficit in risk governance. We therefore conclude that the development and application of integrated governance tools are highly relevant for technology assessment (TA) and TA should actively engage in their development processes.
gangs mit Natur (Kapitel 1), eines "reziproken" Umgangs mit den Mitmenschen (Kapitel 2) und durch den "reflexiven" Umgang mit Sich-Selbst (Kapitel 3) zu bestimmen.In Kapitel 1 fragt er, was im gegenwärtigen Zeitalter des ‚Anthropozäns', in dem die "starre Gegenüberstellung" zwischen Kultur und Natur (S. 23) kaum noch trägt, ‚Natur' ist.Jetzt käme es, so Reheis, darauf an, die gesellschaftlichen Naturverhältnisse so zu regulieren, dass die Regenerationspotenziale der ökologischen Natur nicht überdehnt oder gar ausgeschöpft werden.Mit Verweis auf die sogenannten Managementregeln der Enquete-Kommission "Schutz des Menschen und der Umwelt" des 12. Deutschen Bundestages 1994 zeigt er, dass ein nachhaltiger Umgang mit Stoffen und Energie bedeutet, Material-und Energieumsätze in ein ausgeglichenes Verhältnis zu jenen der ökologischen Systeme zu bringen.Vorsorgendes
In public discussion, high-level nuclear waste is often referred to as an exceptional environmental problem. It is indeed an exceptional problem, but not primarily because of the permanent threat that nuclear waste poses to future generations, as is usually argued. Its exceptionality rather stems from socio-technical factors that create deadlocks and dilemmas, thus hampering and delaying decision-making. This article provides an overview of major socio-technical issues pertaining to nuclear waste, including some that have been neglected in previous literature, and thus contributes to technology assessment in this field.
This article examines the relationship between knowledge and ignorance in the context of crises and corresponding technological solutions. It focuses on the case of pandemic simulation models as a specific form of dealing with uncertainty, which marks a transition from classical risk management to algorithmically organized anticipation practices. The thesis of the paper is that technology assessment is affected by this development when it comes to reflecting on the normative premises and social and political implications of digital crisis technologies. This refers in particular to what is considered crisis-relevant knowledge in the first place, according to what logics it circulates, and what attributions and effects can be observed with regard to digital crisis technologies. Against this background, the paper discusses the relevance of social science knowledge as well as the role of deliberative practices in times of crisis.
Exploring the energy transition The first session chaired by Marcel Weil (KIT) focused on the need for energy transition towards renewable energy sources for sustainable development. Weil’s keynote explored current challenges associated with batteries as energy storage devices. It is evident that for both stationary and mobile energy storage applications batteries will play a significant role as an electrochemical energy storage technology. Particularly, lithium-ion batteries are widely used due to their technical properties, economics, and operability. However, their sustainability is being questioned due to material demand, production costs, environmental and social impacts, technical disadvantages, safety, and end-of-life treatment. Thus, there is a high necessity to develop new battery chemistries that are more environmentally friendly, techno-economically feasible, and disassociated from negative social impacts. Many new concepts such as sodium-ion, magnesium, zinc, and aluminum batteries are emerging with the promise of better sustainability. The following six presentations offered valuable perspectives on evaluating emerging battery technologies: Viera Pechancová (Tomas Bata University) delved into the social sustainability aspects of these technologies. Sebastián Pinto Bautista (KIT) presented prospective life cycle assessments (LCAs) of novel batteries, including Mg-batteries and metaland liquid-free organic batteries, whereas Manuel Baumann (KIT) discussed the toxicity screening of precursor materials for sodium-ion battery cathodes. The presentations reiterated the potential of innovation and emerging technologies, highlighting the importance of assessing their unintended risks, as emphasized in Grunwald’s plenary talk.
The consequences of the COVID-19 pandemic are an accelerator of profound socio-technical transformation processes. Science in general and technology assessment (TA) in particular can and should play an important role in investigating and evaluating these transformation processes and providing robust orientation and transformation knowledge for (political) decision makers and the public. Based on two online surveys “Social consequences of the corona crisis” and data from a citizens’ dialogue, this article examines the assessment of trust in and expectations of science on the part of the TA‑related community and civil society. Lessons for successful TA are synthesized on the basis of inductively derived thematic clusters, such as dealing with uncertain knowledge and ambiguity or the diversity of research approaches.
The use of artificial intelligence (AI) as an innovation driver is increasingly gaining importance among small and medium-sized manufacturing enterprises. In order to enable a successful AI implementation, both the business requirements and the needs of human resources must be considered. One construct that brings these dimensions together is the concept of work ability. So far, there is little scientific evidence addressing work ability in the context of AI implementation. Therefore, this article aims to create a multidimensional framework using the results of a qualitative study on employee-friendly implementation of AI-based systems. The framework combines central aspects (implementation stage, AI-autonomy level, and work ability) and helps to identify suitable recommendations for companies to increase acceptance and trust in the implementation process. Based on the developed framework, a first version of a socio-technical AI support tool has been created.
Societies are facing the challenge of increasing multiple crisis situations, such as the consequences of global climate change, armed conflicts, or pandemics. Policy makers are challenged to find appropriate answers to questions about how to deal with future threats. In the course of the COVID-19 pandemic, numerous experiences were gained with early warning systems used in this context. Based on these experiences, this article discusses how early warning in the political sphere can be improved in the future.
Reflections on the challenges for science in crises have become an integral part of public policy and technology assessment (TA). The urgency and uncertainty of the COVID-19 pandemic brought up the question of how scientific disciplines and individual scientists can provide appropriate advice to decision makers and the public while maintaining transparency and independence. Because of the speed with which solutions had to be found, the range of questions narrowed and some topics were given priority over others. In many countries, decisions were made without broader public participation and without involving the wide variety of stakeholders. In the light of the waning COVID-19 pandemic and the surging climate crisis, it is time to consider how TA, its organizations, and networks can reasonably position themselves to achieve their goals under these conditions. This introduction presents the Special topic of this TATuP issue, in which four research articles explore the role of TA in crises from different perspectives.
of its subject but also in its organizational form, Martina Merz (University of Klagenfurt), proposed three strategies for dealing with ‘organizational complexity’: A segmentation of research infrastructures, the introduction of elements of bureaucratic governance, and the implementation of standards and standardization. Noting that science policy institutions seem to tend towards funding hierarchical rather than egalitarian structures in scientific collaborations, Hanne Anderson (University of Copenhagen) suggested that this preference has epistemic implications since hierarchical structures appear more successful in puzzle solving, but less so in the identification of problems, generation of alternatives and distribution of risks. Presenting a comparative study of two NASA spacecraft collaborations with different internal structures and hierarchies, Janet Vertesi (Princeton University), stressed how the organizational form and resulting culture of a collaboration matter for the scientific outcome. She impressively showed how the different cultures in the two collaborations influenced the selection of data, the information transfer and the discoveries being made. Furthermore, a symposium explored the possible room for and role of creativity in large-scale collaborations. Although the restriction of creativity by the fairly fixed common goals and procedures was identified as potentially epistemically problematic, it was also illustrated how, in large collaborations, individual creativity can take a back seat to an ideal of ‘communal epistemic success’, and how certain procedures can channel creativity.
