We are delighted to publish this Research Topic developed at the initiative of the
7th European Conference on Artificial Intelligence in Finance and Industry Organizing Committee, which welcomes contributions presented at the 2022 event (or past sessions) in the form of extended papers. This Research Topic is the second volume of the Research Topic "Artificial Intelligence in Finance and Industry: Highlights from 6 European COST Conferences". Please see the first volume
here.
Although Artificial Intelligence (AI) as a research topic has now been around for more than half a century, only in recent years it has become a hot topic both in the IT industry and in almost all technology-oriented economic sectors, such as finance and the "traditional" industry. The rapidly increasing computing power has made it possible, even for small and medium-sized companies, to use AI applications in their services and products. Together with the increasing availability of the computational tools needed for successful AI applications, one can also observe an increasing necessity of explainability, contextualization, and the question of responsibility for AI-driven decision making.
This Research Topic aims to show that the possibilities and challenges of AI applications in real-world contexts can be understood only by a bottom-up approach, i.e., by considering as many case studies as possible from as many different economic sectors as possible. Given that almost all economic sectors now claim to be technology-based, it is an urgent necessity to gain an overview of the specific challenges encountered in the practical implementation of abstract AI ideas.
By investigating case studies from the financial and the real economy, we intend to show that the fundamental principles underlying these case studies, and also the business challenges in the implementation of AI applications have a common core. This common core, however, cannot be understood by discussing the underlying principles on a purely theoretical or algorithmic level but only becomes apparent when the theoretical principles are confronted with the business challenges arising from their transformation into successful products and services. The increasing complexity of the data-processing techniques used in AI applications simultaneously creates a growing need for explainability of their results in order to keep trustworthiness and also to gain acceptance outside the inner circle of technology specialists.
Researchers and practitioners from academic institutions and private companies are welcome to contribute with original research articles and other papers that show innovative case studies of AI applications in a financial or industrial context. Papers on specific topics of interest include but are not limited to:
· complete case studies,
· descriptive studies of particular challenges in the implementation,
· business cases for introducing such AI applications,
· discussions on the transition from traditional optimization to AI methods,
· review papers on the process of acceptance of AI methods among customers and other stakeholders of the company, but also the general public,
· manuscripts on ethical and legal issues arising from the implementation and use of such applications.
