AI: Back to the Roots? (original) (raw)

1.1 Explainable AI

Guest Editors: Ute Schmid (Universität Bamberg), Britta Wrede (Universitát Bielefeld)

Scope: During the last years, Explainable AI (XAI) has been established as a new area of research focusing on approaches which allow humans to comprehend and possibly control machine learned (ML) models and other AI-systems whose complexity makes the process which leads to a specific decision intransparent. In the beginning, most approaches were concerned with post-hoc explanations for classification decisions of deep learning architectures, especially for image classification. Furthermore, a growing number of empirical studies addressed effects of explanations on trust in and acceptability of AI/ML systems. Recent work has broadened the perspective of XAI, covering topics such as verbal explanations, explanations by prototypes and contrastive explanations, combining explanations and interactive machine learning, multi-step explanations, explanations in the context of machine teaching, relations between interpretable approaches of machine learning and post-hoc explanations, neuro-symbolic approaches and other hybrid approaches combining reasoning and learning for XAI. Addressing criticism regarding missing adaptivity more interactive accounts have been developed to take individual differences into account. Also, the question of evaluation beyond mere batch testing has come into focus.

In the special issue, the focus will be on research addressing such recent developments in XAI. Furthermore, interdisciplinary contributions as well as specific applications of XAI form domains such as education, healthcare, and industrial production are welcome.

The topics of interest for the special issue include, but are not limited to:

Contributions can be from the following categories (for more detailed information please refer to the author instructions for each of these categories): Technical Contribution; System Descriptions; Project Reports; Dissertation and Habilitation Abstracts; AI Transfer; Discussion

If you are interested in submitting a paper please contact one of the guest editors:

Contact Ute Schmidt ute.schmid@uni-bamberg.de

1.2 GeoAI

Guest Editors: Simon Scheider, Zena Wood, Kai-Florian Richter

Scope: Researchers in Artificial Intelligence (AI) and Geography have been developing various points of contact in the past, with many possibilities of mutual benefit in the future. Recently, subsymbolic AI methods, such as Deep Learning, have increased the quality and scalability of data processing methods in remote sensing, geographic information retrieval, natural language processing (NLP) and geospatial modeling, among others. Furthermore, there is a tradition of using symbolic AI approaches to raise the quality and scalability of methods by linking, e.g., Geography with agent-based simulation (ABM), spatial cognitive reasoning with Robotics, as well as Geography with the Knowledge Graphs (KG) in the Semantic Web. At the same time, geographic information has become an indispensable resource in itself, needed not only for adding spatial intelligence to machines, and for making opaque models transparent, but also for understanding what kind of intelligence is needed to refer to place and to handle space. Understood in this broader sense, geoAI has the potential of fundamentally improving the way geographic information can be processed and interpreted by both humans and machines.

For this special issue, we invite researchers who investigate the kind of knowledge needed to account for Geography and space with(in) intelligent machines. We are looking for original research articles, project reports and discussion articles on (among others):

Application areas include, but are not restricted to:

Contacts: Simon Scheider (s.scheider@uu.nl),Zena Wood (Z.M.Wood2@exeter.ac.uk),Kai-Florian Richter (kai-florian.richter@umu.se)

1.3 AI in Current and Future Agriculture

Guest Editors: Joachim Hertzberg, Jan Christoph Krause, Benjamin Kisliuk

Scope:

Agriculture is a perfect field for applying AI technology: uncertainty, data-rich and knowledge-rich applications and a high degree of digitalization in today’s farming technology. Today, assistive technologies as seen in precision agriculture, farm management systems and monitoring systems improve existing processes and improve their performance, while various robots have been in use in animal husbandry and start getting used in crop farming. Still, there is a lack of fully automated and integrated solutions for conventional agriculture which would transform practical procedures. Further, alternative cultivation concepts like agroforestry, spot farming and mixed cultivation approaches could become feasible by AI in the first place. For allowing AI to enable this transformation in agriculture, advances would be required in the fields of perception, navigation, autonomy, learning, data analysis, inference and (multi) robot control. Besides improving the technology, compliance with ethical, legal and social implications is vital for putting AI further into practice as well as to increase acceptance of users as of society at large. This Special Issue aims at providing an overview of work in AI in agriculture regarding, but not limited to, the following topics. All submissions will be peer- reviewed:

Contributions can be from the following categories (for more detailed information please refer to the author instructions for each of these categories): Technical Contribution; System Descriptions; Project Reports; Dissertation and Habilitation Abstracts; AI Transfer; Discussion

Contact: Benjamin Kisliuk (DFKI), benjamin.kisliuk@dfki.de