Based on a large scale of technology application scenarios, artificial intelligence (AI) is expected to have disruptive impact on economies and societies. In recent years, breakthroughs have been made in basic research on the fundamental technologies of artificial intelligence. AI is showing greater potential to become a general-purpose technology. Major economies are focusing on policies, regulations, and strategic plans around basic research and R&D of technology application scenarios in AI. However, the optimization of AI policy-making demands more interdisciplinary knowledge and a broader societal debate. In the domain of technology assessment (TA), the research on AI and its potential impacts has been considered important already early. The research questions, which include impacts on the workforce as well as impacts on societal communication and democracy as well as fundamental issues like responsibility, transparency, and ethics, have drawn widespread attention from TA studies. This chapter presents a scholarly discussion of AI topics in the context of TA, based on a qualitative analysis of AI policy databases from the OECD and EPTA. The analysis concludes that enhancing global cooperation in TA will contribute to address fundamental ethical and societal issues of AI, which in turn broadens the knowledge base and helps to pave way for a more inclusive and just use of AI.

Car-hailing platform governance is an emerging topic of research and practice. The governance of the data-driven platform economy is challenging the research paradigm of competition regulation in the context of open innovation. This research is trying to reveal the market allocation structure of China’s online car-hailing industry from the perspective of personal data allocation by the study of Application Programming Interface (API) of sample platforms. On the basis of the networked nature of personal data allocation via APIs, this research constructs a mathematical model of the edge weight of data resource connections between platforms. Furthermore, this research optimises the structural hole analysis of complex networks to discuss the state of personal data resource allocation in China’s car-hailing industry. Results reveal that there are obvious structural holes within the sample network. When compared with related indicators, we found that accessing personal data resources is an essential component of the sample network competition capability and sustainable innovation. Social media platforms and online payment platforms more greatly impact car-hailing platform competition than other types of platforms within the multi-sided market context. This research offers a research perspective of personal data allocation for further study of competition, regulation and sustainable innovation of data-driven platform economies.

The missions of Technology Assessment (TA), Responsible Research and Innovation (RRI) and Sustainability Research (SR) are similar in their relationship to Science, Technology and Innovation (STI). Although adopting different terminology, the three research fields show great conceptual and methodological overlaps, as all three react to societal demands and policy needs via common tools. The impact of TA, RRI and SR is linked directly, but its assessment requires new thinking in terms of common conceptual and methodological approaches. The paper provides an overview of the current discussion on societal impact assessment and identifies areas of particular interest for the three fields. It describes the current discussions on impact assessment and impact indicators in each field and undertakes a bibliometric analysis that shows clear inter-relationships in terms of thematic focus and a common emphasis on impact in all three fields. Following this analysis, the paper argues for a common impact assessment methodology for TA, RRI and SR, under the notion of resonance and based on the concepts of anticipation, reflection and inclusion.

The current rapid development of online car-hailing services creates a serious challenge to the existing paradigm of market governance and antitrust policy. However, the debate on the market structure of the car-hailing platform requires more empirical evidence to uncover its functions. This research adopts an interdisciplinary methodology based on computer science and economics, and including software reverse engineering tools applied to the interoperability of the terminal application and resource allocation model, to demonstrate the topological market structure of personal data resources allocation in China’s car-hailing industry. Within the discussion of the hybrid nature of technology and economy, the analysis results clearly show that China’s car-hailing platform services present a multi-sided market structure when seen from the perspective of personal data resource allocation. Personal data resource (PDR), that is considered an essential market resource, is applied as an asset transferred unhindered between platforms via the application programming interface, and thus, creating a new market allocation mechanism. The connection between the car-hailing platforms and social media platforms is an essential aspect of the market competition in the domain. As applications of online platforms increase in the global context, this research offers a new perspective in personal data resource allocation with implications for the governance of the platform economy.