Short abstract:

In this presentation we analyze recent ‘systemic turns’ across public engagement and responsible innovation literature calling for more holistic approaches that can investigate the wider institutional contexts that shape public engagement itself.

Long abstract:

In exploring ways of doing science with, rather than for publics, public engagement and responsible innovation research is vital in democratizing science. However, in recent case-study based research that dominates the public engagement literature, authors raise questions about the field’s intertwinement with the institutional systems it simultaneously tries to transform.

To clarify how the field understands its own positionality in democratizing science, in this presentation we will analyze recent ‘systemic turns’ across public engagement and responsible innovation literature calling for more holistic approaches that can investigate the wider institutional contexts that shape public engagement itself.

We present persistent – and yet, equally ambiguous and contradictory – notions of institutional systems as rather static and inherently powerful. While literature about power is widely available in the field, there are insufficient narratives that recognize public engagement and responsible innovation research as rooted in power. We argue for a more fundamental reconfiguration of acting ‘in the name of’ science to account for the vantage points from which such research produces and democratizes science.

Short abstract:

This presentation will discuss how a growing number of ethical principles and guidelines inform governance practices within public-private ecosystems centered on the twin Digital and Energy transition, drawing on empirical research within an interdisciplinary collaborative research project.

Long abstract:

The use of Artificial intelligence (AI) systems in the electricity sector is increasing, as part of the twin Digital and Green transitions in the EU. The expectation is that it can help deal with some of the most pressing challenges of the energy transition (decentralization, unpredictability of electricity sources, growing demand for electricity), providing more accurate forecasts and predictions to support investment in infrastructures, keeping energy networks in balance, managing flexibility assets, helping consumers to save energy, and more. In their efforts to develop AI-based innovative solutions for electricity systems, public-private collaborations are situated between different and sometimes conflicting discourses centering on the evolving regulatory and ethical frameworks that shape the twin transitions. This is illustrated by the growing number of ethical guidelines and principles intended to steer developments in either the energy sector or the field of AI. This presentation will discuss how such guidelines and principles can inform governance and development practices within public-private ecosystems. It draws on empirical research within a collaborative research project, where university and industry partners aim to develop innovative AI-based self-management systems at the electricity grid edge. Moreover, the presentation will reflect on some of the interventions that have been made within this collaboration. One such intervention is the outlining of a governance framework for AI within an energy ecosystem to ensure that core values are safeguarded. Part of this intervention has been to explore how such core values can or should be identified and how the different parties align around these values.

Short abstract:

We provide empirical insights into, and reflection on, our own attempt to practice responsible innovation ecosystem governance in NeuroSys, a research and innovation project in the area of next-generation computing. We introduce and argue for the concept of transformative innovation ecosystems.

Long abstract:

Responding to calls for a systemic turn in Responsible Innovation (RI) and an emerging body of literature on ‘responsible’ innovation ecosystems, Smolka and Böschen (2023) have introduced the concept of responsible innovation ecosystem governance. However, there is little empirical research on the practicality of such a ‘systemic’ approach to RI. We therefore provide empirical insights into, and critical reflection on, achievements and challenges in our own attempt to practice responsible innovation governance in the emerging innovation ecosystem of NeuroSys. NeuroSys is a multidisciplinary research and innovation cluster, in which scientists, industry actors, and regional stakeholders collaborate to develop and commercialize brain-inspired computing hardware and software that promise to improve the energy-efficiency and performance of artificial intelligence (AI) applications. In a team of embedded social scientists and ethicists, we seek to integrate ethics and societal considerations into high-tech research and innovation, drawing on multi-method approach of anticipation and intervention. A significant challenge for a responsibility-oriented innovation ecosystem is to simultaneously achieve sociotechnical viability beyond the sociotechnical niche and sociotechnical desirability in light of multiple, interdependent unfolding transformation processes and questions of (global) justice. To better understand these challenges and ways of addressing them, we introduce and argue for the concept of transformative innovation ecosystems.

Short abstract:

This paper develops a framework identifying key features for an effective and democratic knowledge ecosystem. It evaluates the Dutch case of radioactive waste management, contributing empirically and theoretically to the discourse on knowledge ecosystems and similar concepts.

Long abstract:

Society faces grand societal challenges like climate change and biodiversity-loss, often referred to as wicked problems. In addressing these challenges, the role of knowledge is crucial yet frequently subject to debate. Therefore, enhancing our understanding of how knowledge can effectively contribute to addressing sustainability issues becomes increasingly imperative. The long-term management of radioactive waste exemplifies such a complex challenge, characterized by intergenerational and multi-level governance aspects and a diversity of values and uncertainties. Ensuring that knowledge effectively informs decision-making on this matter is both important and complex. Academic literature argues the necessity of an effective and democratic knowledge ecosystem for addressing wicked problems. However, existing literature lacks a clear answer regarding what constitutes a democratic and effective knowledge ecosystem, as well as which interventions can enhance it. This paper addresses this gap by developing a framework through an explorative literature review, synthesizing key features for an effective and democratic knowledge ecosystem. These features are organized around the involved actors and their knowledge, their interactions, and the governance structure, collectively shaping the knowledge ecosystem. Subsequently, the paper reflects on insights gained from a stakeholder workshop, where this framework was applied to evaluate the Dutch knowledge ecosystem concerning long-term radioactive waste management. The analysis sheds light on interventions that can enhance the effectiveness and democratic nature of this knowledge ecosystem. The discussion explores opportunities for generalization. Consequently, this paper contributes both theoretically and empirically to the growing discourse on knowledge ecosystems and similar concepts, while also pinpointing possibilities for future research.

Long abstract:

STS scholars have described the capitalization of the life sciences and identified a trend toward the financialization and assetization of biomedical R&D following twin crises in the early 21st century. Biomedicine remains big business; hopes for both treating disease and anchoring regional and national economic development hang on its promise. As a shift toward personalized and precision medicine reshapes the intellectual property landscape in biomedicine, the typical technology transfer model has evolved into a complex “innovation ecosystem” consisting of public-private and university-startup partnerships. These changes risk occluding sites of ethical governance and the visions of the public good and public benefit that animate and derive from contemporary biomedicine. Universities and academic medical centers—which often serve as innovation ecosystems’ “anchor institutions”—have tripartite missions that include public service and clinical care. It remains important to understand how these institutions understand and pursue these aims as large amounts of collective resources flow into biomedical innovation ecosystems that focus on tailored and high-cost treatments. This is especially important as struggles over the function and governance of universities are at a fever pitch. This talk describes in-progress research furthering recent STS scholarship on biocapital and bioeconomies, using an ethnographic approach to identify the practices of valuation and understandings of public good and public benefit that circulate in a precision medicine innovation ecosystem. It does so in the hope that the governance decisions that are made in these ecosystems can become more transparent, democratic, and just.

Long abstract:

Applying ICT, artificial intelligence, and machine learning technologies to medical device products has created significant challenges to transforming the medical device sector from traditional medical devices to intelligent medical devices. How do institutional factors facilitate the transformation of the medical device innovation system? This research adopts the national biotechnology sectoral innovation system as the main framework. The study adopts a mixed-method approach, combining the analytical hierarchical process, in-depth interview, scitometric mapping, and documentary analysis to explore the key factors that will transform and develop Taiwan's smart medical device industry. The results show that the most critical criteria affecting the transformation of Taiwan's intelligent medical device innovation system are the stock of knowledge, which includes technological accumulation in related sectors, national funding of basic research, and national scientific education. From scientometric mapping on the data gathering from Mesh-term, we found the medical device sector recently focusing on equipment development and reagent kits diagnostic development. The Taiwanese government has started incentives, investing resources and funds to support the development of the medical device sector. However, the pre-market safety evaluation regulations are not well-developed, which challenges the commercialization process of technology research and development. This paper will be one of the very few papers combining the framework of the national biotechnology innovation system with the analytical hierarchical process. Overall, this paper will be of interest to offer insights into Taiwan's strategies and initiatives to enhance the policy framework and the competitiveness of global intelligent medical device development.

Long abstract:

Artificial intelligence (AI) technology is at the center of the contemporary scientific and technological revolution and industrial transformation, driving changes in the global innovation landscape and economic structure. As an active participant in the world, China has already made remarkable achievements in the development and governance of AI technology.

On the one hand, the Chinese government attaches great importance to the development of AI, and has issued a series of policies, such as the New Generation Artificial Intelligence Development Plan, which clearly defines the development direction and goals of AI and provides strong policy support for the development of AI. In terms of R&D investment, Chinese companies and research institutions have been increasing their R&D investment in the field of AI, which has promoted continuous innovation and breakthroughs in AI technology.

On the other hand, China is also exploring and innovating in the governance of AI technology. For example, China has introduced AI ethical norms and governance principles, clarifying the bottom line and moral code for the development of AI, which provides an important guarantee for the healthy development of AI.

This paper explores the impact of governance on innovation ecosystems through an empirical study. We analyze policy documents, publications, patents, and other materials, combined with interviews with researchers at AI research institutions and regulators at government departments, to analyze the forms of governance and the capabilities needed by different institutions in the innovation ecosystem.

Long abstract:

When discussing generative AI systems such as ChatGPT, it is common to discuss them as if they are a single universal system. Although this may be true now, it is unlikely to remain the case. Generative AI systems are products of the data they are trained on, the legality of which has begun to vary. This paper explores how developing data ecologies are affecting regional generative AI implementation. Data ecologies refer to distinctive regulatory environments governing data flows. Data ecologies come into friction with one another when a product is designed in one data ecology but utilized in another. For example, ChatGPT is currently banned in Italy, where it has been accused of violating Italian/EU data privacy laws.

This paper builds from current literature examining the effects of different data ecologies on the targeted/surveillance advertising space. We explore how the effect emerges differently —and with potentially greater economic ramifications— in the case of generative AI due to the productive nature of generative AI. Our analysis is positioned around the regulatory framing of training data, including personal data, non-personal data, and copyrighted material. To do so, we provide a comparative analysis of four cases. 1) The European Union 2) The United States 3) Japan 4) China. Our comparative analysis is positioned to flesh out how each of these regulatory regimes are developing regulations to govern the flows of data that may be used in generative AI systems and highlight how their choices create geopolitical and economic friction.

Long abstract:

The question how technology and society can be aligned, has been a topic for policy-makers and (STS) scholars for over half a century. Technology assessment (TA) was put forward to contribute to better alignment. Ever since the installment of the Office of Technology Assessment in the United States in 1972, the practice of TA has been institutionalized in many parts of the world. In the face of emerging (policy) problems, technological developments and scholarly insights, understandings of the role TA in fostering the alignment between technology and society have changed. Important reorientations include: which actors should be included or addressed, what types of issues should be considered, and when should interventions take place? Such reorientations expanded the plethora of TA-inspired interventions in a nested way, building on each other, rather than competing with or replacing existing modes. Accordingly, a wide variety of TA-inspired interventions are practiced at this day in time, which collectively may contribute to a 'technology governance ecosystem'.

However, the quest towards alignment of technology and society is far from completed. Accordingly, it is crucial to reflect on the desired evolution of TA - and ultimately an effective technology governance ecosystem. Learning from the historical development of TA is pivotal in this regard. This contribution presents Kingdon’s multiple streams framework (MSF) as a valuable framework to facilitate such learning. Drawing from experience in the Dutch TA context, this contribution aims to discuss the conceptual and historical development of TA, by applying the MSF to TA in the Netherlands.

Long abstract:

Innovation is invoked as the solution to myriad social challenges. Yet, there is growing criticism of technology, especially focused on the big four–Apple, Amazon, Microsoft, and Meta. Those companies are accused of seeking profit at the expense of humans and the environment. Alongside the rise of those companies, the theories and practices of responsible innovation have evolved out of constructive technology assessment and other scholarship. Policy implementation of responsible research and innovation (RRI) is seen in the UK as the AREA framework and in the European Union. In other parts of the world there is less formal adoption of responsible innovation. This led to the question: Do the responsibilities expressed by leaders in industry, government, and academia align with the human values associated with responsible innovation? To answer this question, approximately 100 organizational leaders were interviewed and expressed 2,300 discrete statements of responsibility for innovation. Those statements were thematically coded by six research assistants and interrater reliability was validated using Krippendorff’s Alpha. This is a novel method to assess innovation based upon the human values that underpin the responsibilities for innovation. This research offers an alternative to economic-output models to assess innovation by counting investments, patents, and publications. The results show evidence that the practice of responsible innovation is present, yet remains overshadowed by a myopic focus on wealth creation. This presentation will critically reflect on the role of science, technology, and society scholars to practice “bold modesty” and contribute to the reorientation of innovation toward broader human values.

Long abstract:

Novel genetic biocontrol technologies, such as gene drives, are being developed for potential use in pest control, conservation, and public health. The ecosystems within which gene drives are developed and potentially deployed are therefore richly populated by individuals and groups whose relationships and practices are likely to be (re-)shaped by the technology. Responsible innovation requires that developers meld social and technical considerations in their designs to meet community expectations regarding both functionality and broader values. In other words, developers must consider what it will be like to live with gene drives, and how their design choices might support positive experiences.

In this paper I explore experience as a key feature of the innovation ecosystem concept, and as a basis for gene drive design. To do so, I draw on empirical research using variety of systems tools to qualitatively map the disruptive potential of a murine gene drive in South Australian pest management and conservation. By centering experience, I aim to expand and enrich the innovation ecosystem concept by highlighting subtle sources of resistance and support, which are rooted in people's day-to-day lives.

This research presents empirical findings that are relevant for the wider implementation of the innovation ecosystem concept. Furthermore, the research and its findings have implications for the field of responsible research and innovation, particularly regarding the application of experience-centred technology design.

Long abstract:

Structural change is accompanied by three challenging dynamics: exnovation, innovation and transformation. The special feature here is that these three processes should ideally be synchronized. However, empirical experience shows that this rarely happens. To the contrary, in most cases there is a focus on selective innovation processes seen to fuel best the ‘economic pump’. This strategy is easy to implement, but risky with regard to the possible outcomes for regional change.

Against this background, this paper will focus on a process of structural change that is currently taking place. Structural change in the Rhenish mining area. The subject matter is the different processes of infrastructuration taking place simultaneously in this context. The aim of the paper is twofold. On the one hand, a specific concept of innovation ecosystem is discussed in which the parallel development of infrastructures in the context of structural change and overarching transformation processes is examined and accentuated. On the other hand, a contrasting analysis is carried out on the basis of four selected innovation-exnovation fields in order to make the dynamics of infrastructuration visible in their interplay.