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Fwd: [AISWorld] [IEEE ICHMS 2021] CfP: Intl. Summer School on Situation Awareness <> CfP: Late Braking Papers, PhD & Demo Track - July 19th
by Gustaf Neumann 30 Jun '21

30 Jun '21

-------- Forwarded Message -------- Subject: [AISWorld] [IEEE ICHMS 2021] CfP: Intl. Summer School on Situation Awareness <> CfP: Late Braking Papers, PhD & Demo Track - July 19th Date: Tue, 29 Jun 2021 21:47:25 +0200 From: Andreas Nuernberger <andreas.nuernberger(a)ovgu.de> To: aisworld(a)lists.aisnet.org * Call for Participation * **** Please forward this e-mail to potentially interested students/researchers **** International Summer School on Situation Awareness in Cognitive Technologies 2021 Theory and Application September 6-11th, 2021 at Magdeburg, Germany: http://isact.cogsy.de/ In conjunction with ICHMS 2021: 2nd IEEE International Conference on Human-Machine Systems (ICHMS 2021) Magdeburg, Germany, 8-10 September 2021 (Hybrid Event) Late Braking Papers, PhD & Demo Track open until July 19th: https://www.ichms2021.de *** Scope of the Summer School: *** We are pleased to announce that the International Summer School on Situation Awareness in Cognitive Technologies (ISACT 2021) will be held from September 6 to 11, 2021 in Magdeburg, Germany, in conjunction with the IEEE International Conference on Human-Machine Systems (ICHMS 2021). Situation Awareness in Cognitive Technologies is a trending research direction that gained a lot of interest from the industry in recent years. Therefore, the summer school aims to present the fundamental aspects of situation awareness in cognitive technologies which can be discussed in an interdisciplinary context. There are a couple of aspects to consider: Situation Awareness, Cognitive Technologies, Human-Computer Interaction (HCI) and Explainable AI (XAI). Especially the HCI context provides a link between theory and application which receives special attention in this summer school along with the recent trends in Explainable AI. To make this school an interactive learning experience, we would encourage the participants to share their research presentations (posters) related to the above-mentioned topics. This year’s edition intends to bring together academia and industry to provide a large practical perspective to undergraduate and graduate (including early-stage PhD) students, as well as to young industry personnel. Attendees will be able to extend their knowledge in both theoretical and practical aspects of: - Situation Awareness - Cognitive Technologies - Brain-Computer Interaction - Human-Computer Interaction - Explainable AI The International Summer School on Situation Awareness in Cognitive Technologies is a great opportunity to learn about new technologies, meet fellow students/employees, discuss ideas with experts, participate in an international conference and simply have a splendid time in the city of Otto von Guericke! *** Important Dates: *** Application Deadline: July 19th, 2021 Notification of Acceptance: July 28th, 2021 Summer School Date: September 6-11th, 2021 *** Submissions: *** The application deadline is July 19th, 2021. Please fill out the application form at isact.cogsy.de/apply and upload your CV to isact.cogsy.de/cv. Moreover, we also accept applications for travel grants. The travel grant intends to cover the travelling costs to the summer school's venue. Therefore, the application must provide an additional list of estimated travel costs to Magdeburg. For further information please refer to isact.cogsy.de. We invite applicants to submit an accompanying paper in the PhD Track of ICHMS 2021 (https://www.ichms2021.de). Activities of both events will be synchronized so that participation at selected sessions is possible. _______________________________________________ AISWorld mailing list AISWorld(a)lists.aisnet.org

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Fwd: [wkwi] CfP Special Issue "Trust in AI for electronic markets"
by Gustaf Neumann 29 Jun '21

29 Jun '21

-------- Forwarded Message -------- Subject: [wkwi] CfP Special Issue "Trust in AI for electronic markets" Date: Tue, 29 Jun 2021 10:48:18 +0200 From: editors(a)electronicmarkets.org Reply-To: editors(a)electronicmarkets.org To: wkwi(a)listserv.dfn.de . CfP Special Issue "Trust in AI for electronic markets" <https://ocis.aom.org/ocis/communities/community-home/digestviewer/viewthrea…> --- Apologies for cross-postings--- Call for Papers: "*Trust in AI for electronic markets*" in "Electronic Markets - The International Journal on Networked Business" Submission deadline: December, 15, 2021 *Guest Editors* Wolfgang Maass, Saarland University and German Research Center for Artificial Intelligence (DFKI), Germany, wolfgang.maass(at)dfki.de Roman Lukyanenko, HEC Montréal, Canada, roman.lukyanenko(at)hec.ca Veda C. Storey, Georgia State University, USA, vstorey(at)gsu.edu *Theme* Electronic markets for trading physical, as well as digital, goods offer a wide variety of services based on Artificial Intelligence technologies, as smart market services. Smart market services generate recommendations and predictions using Artificial Intelligence (AI) technologies on data available and accessible in electronic markets. For instance, financial high-speed trading is only feasible by smart market services that autonomously execute transactions according to market signals based on AI models trained with big data. Electronic marketplaces, including Amazon and Alibaba, are using AI technologies to provide smart services to consumers, optimize logistics, analyze consumer behavior, and derive innovative product and service designs. Some business leaders even consider there to be major threats to society from sophisticated AI solutions, while using AI extensively for their own business. Because AI systems elude human understanding and scrutinization, trust in AI is crucial for the success of smart market services, as well as other AI or machine learning-based systems . Gaining trust in AI begins with transparency in the reviews of (a) data so that biases and gaps in knowledge of a domain are controlled, (b) AI models and objective functions, (c) model performance and (d) results generated by AI models for decision making. Trust becomes an important factor for overcoming uncertainty on AI-based recommendations in general and in electronic markets in particular. The quality of smart market services depends on shared understanding and conceptual models of data used for training AI models; data quality; the selection and training of appropriate models; and the embedding of models into smart market services. Providers of smart market services are required to build trust relationships with business and end customers based on limited possibilities for opening the "black boxes" of Artificial Intelligence systems due to increased complexity of machine learning models. Empirical studies on trust in AI indicate heterogenous results. Companies and end-users appreciate benefits and opportunities provided by smart market services. At the same time, concerns are raised with respect to privacy issues and biases of data, models and algorithms. Overly optimistic customers might become disappointed if smart market services do not deliver as expected. Proof of privacy leaks and biases might reinforce prejudices. Both may lead to decrease of trust in AI. Challenging research questions are to identify which methods, indicators and experiences have increasing effects on trust in AI. For instance, explainable AI is a technical means for opening "black boxes" of AI systems, generally, and smart market services, specifically. This special issue seeks contributions on trust in Artificial Intelligence in the context of electronic markets. Contributions that help to understand challenges from an economic, legal or technical perspective are invited. *Central issues and topics* Possible topics of submissions include, but are not limited to: * Trust behavior and AI * Mental models, conceptual models and AI models * Psychological and sociological factors for trust in AI * Human-centric design of smart market services * Explainable AI for smart market services * Threats for trust in AI * Frameworks for smart markets * Business and legal aspects influencing trust in AI * Relationships between trust and Business models with smart market services * Transparency of data, AI models and recommendations * Case studies on building trust in AI *Submission*: Electronic Markets is a Social Science Citation Index (SSCI)-listed journal (IF 2.981 in 2019) in the area of information systems. We encourage original contributions with a broad range of methodological approaches, including conceptual, qualitative and quantitative research. Please also consider position papers and case studies for this special issue. All papers should fit the journal scope (for more information, see www.electronicmarkets.org/about-em/scope/ <http://www.electronicmarkets.org/about-em/scope/>) and will undergo a double-blind peer-review process. Submissions must be made via the journal's submission system and comply with the journal's formatting standards. The preferred average article length is approximately 8,000 words, excluding references. If you would like to discuss any aspect of this special issue, you may either contact the guest editors or the Editorial Office. *Keywords*: Trust, Interpretability, Mental Models, Conceptual Models, Explainable AI, Smart Market Services, Privacy, Fairness of Artificial Intelligence, Biases, Transparency *Important deadline: ** Submission Deadline: December, 15, 2021 *References* Domingos, P. (2012). A few useful things to know about machine learning. Communications of the ACM, 55(10), 78-87. doi.org/10.1145/2347736.2347755 <https://doi.org/10.1145/2347736.2347755>. Dwivedi, Y. K. et al. (2019). Artificial intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International Journal of Information Management, 57, 101994, doi.org/10.1016/j.ijinfomgt.2019.08.002.https://... <https://doi.org/10.1016/j.ijinfomgt.2019.08.002.https:/www.sciencedirect.co…> Jacovi, A., Marasovi, A., Miller, T., & Goldberg, Y. (2021). Formalizing trust in artificial intelligence: Prerequisites, causes and goals of human trust in ai. In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, pp. 624-635. doi.org/10.1145/3442188.3445923 <https://doi.org/10.1145/3442188.3445923>. Luo, X., Tong, S., Fang, Z., & Qu, Z. (2019). Frontiers: Machines vs. humans: The impact of artificial intelligence chatbot disclosure on customer purchases. Marketing Science, 38(6), 937-947. doi.org/10.1287/mksc.2019.1192 <https://doi.org/10.1287/mksc.2019.1192>. Maass, W., Parsons, J., Purao, S., Storey, V. C., & Woo, C. (2018). Data-driven meets theory-driven research in the era of big data: opportunities and challenges for information systems research. Journal of the Association for Information Systems, 19(12), 1. doi.org/10.17705/1jais.00526 <https://doi.org/10.17705/1jais.00526>. Maass, W., Parsons, J., Purao, S., & Storey, V. C. (2021). Pairing conceptual modeling with machine learning. Data & Knowledge Engineering, forthcoming. Maass, W., Storey, V. C., & Lukyanenko, R. (2021). From mental models to machine learning models via conceptual models. In Exploring Modeling Methods for Systems Analysis and Development (EMMSAD 2021), Melbourne, Australia, pp. 1–8. doi.org/10.1007/978-3-030-79186-5_19 <https://doi.org/10.1007/978-3-030-79186-5_19>. Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). "Why should I trust you?" Explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135-1144. dx.doi.org/10.1145/2939672.2939778. Siau, K., & Wang, W. (2018). Building trust in artificial intelligence, machine learning, and robotics. Cutter Business Technology Journal, 31(2), 47-53. Thiebes, S., Lins, S., & Sunyaev, A. Trustworthy artificial intelligence. Electronic Markets, 31(2021)2. doi.org/10.1007/s12525-020-00441-4 <https://doi.org/10.1007/s12525-020-00441-4>. ==================================================================== *Electronic Markets - The International Journal on Networked Business* Editors-in-Chief: Rainer Alt, Leipzig University and Hans-Dieter Zimmermann, FHS St.Gallen, University of Applied Sciences, Executive Editor: Ramona Coia, Leipzig University *Editorial Office* c/o Information Systems Institute Leipzig University 04109 Leipzig, Germany Mail: editors(a)electronicmarkets.org <mailto:editors@electronicmarkets.org> Phone: +49-341-9733600 http://www.electronicmarkets.org <http://www.electronicmarkets.org> www.facebook.com/ElectronicMarkets <https://www.facebook.com/ElectronicMarkets> twitter.com/journal_EM <https://twitter.com/journal_EM> www.springer.com/journal/12525 <https://www.springer.com/journal/12525> Journal Impact Factor 2019: 2.981

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Fwd: [AISWorld] Call for papers AIOPS 2021
by Gustaf Neumann 29 Jun '21

29 Jun '21

-------- Forwarded Message -------- Subject: [AISWorld] Call for papers AIOPS 2021 Date: Tue, 29 Jun 2021 07:59:39 +0000 From: Bogatinovski, Jasmin <jasmin.bogatinovski(a)tu-berlin.de> To: aisworld(a)lists.aisnet.org <aisworld(a)lists.aisnet.org> CALL FOR SUBMISSIONS Second International Workshop on Artificial Intelligence for IT Operations collocated with The 19-th International Conference on Service Oriented Computing (ICSOC 2021) Dubai, UAE, November 22, 2021 >>> https://aiops2021.github.io/ <<< SUBMISSIONS ARE NOW OPEN at: https://easychair.org/conferences/?conf=aiops2021 The official Call for Papers can be found at: https://easychair.org/cfp/AIOPS2021 Submission deadline: September 30, 2021 Scope Large-scale IT systems, such as data centres, cloud computing environments, edge clouds, IoT and embedded environments, are the key enablers of digital transformation. Managing such systems puts an enormous burden on the operators in dealing with the abundance of data, oftentimes leading to severe economic implications. To mitigate this issue IT operators increasingly rely on tools from artificial intelligence for assistance in the operation of IT systems. Artificial Intelligence for IT Operations (AIOps) is an emerging field arising in the intersection between the research areas of machine learning, big data, streaming analytics, and the management of IT operations. The main goal is the analysis of system information of heterogeneous type (metrics, logs, customer input, etc) to support administrators by optimizing various objectives like prevention of SLA violation, early anomaly detection and auto-remediation, energy-efficient system operation, providing optimal QoE for customers, predictive maintenance and many more. In this field, a constantly growing interest can be observed, and thus, practical tools are developed from both the academy and industry sector. We envision that, with the advance of AIOps technologies, the IT industry will achieve significant progress and sustained and exponential growth. The main focus of this workshop is to bring together researchers from both academia and industry to present their experiences, results, and work in progress in this field. A major part of the workshop is to strengthen the community and unite it towards the efforts for solving the main challenges. Topics of interest are the following: * Early anomaly, fault and failure (AFF) detection and analysis * Software dependability * Self-healing, self-correction and auto-remediation * Self-adaptive time-series based models for prognostics and forecasting * AFF identification, localization, and isolation * Root cause analysis * Adaptive fault tolerance policies * Forecasting of hardware and process quality * Performance management * Planning under uncertainty * Predictive and prescriptive maintenance * Maintenance scheduling and on-demand maintenance planning * Alarm correlation * Log analysis * Fault-tolerant system control * Resiliency, reliability, and quality assurance * Autonomic process optimization * Energy-efficient cloud operation * Distributed resource management * Autonomous service provisioning * Visual analytics and interactive machine learning * Active and life-long learning * Design of experiment (DoE) and benchmarking * Fault injection and chaos engineering * Use-cases, testbeds, evaluation scenarios Publication: Post-workshop proceedings in Springer’s LNCS series. Venue: collocated with ICSOC 2021 conference in Dubai 22 November 2021. More information: http://www.icsoc.org/ <http://www.icsoc.org/> , AIOPS2021 (aiops2021.github.io/) Important Dates: Paper submission deadline: September 30, 2021 Acceptance notification: October 30, 2021 All deadlines are in Samoa Standard Time (SST = GMT – 11). Check the time in the SST Zone here: https://time.is/SST. Contact Questions about submissions: aiops2021(a)googlegroups.com <mailto:aiops2021@googlegroups.com> Organizers Roberto Natella, University of Naples Federico II, Italy Jasmin Bogatinovski, Technical University Berlin, Germany _______________________________________________ AISWorld mailing list AISWorld(a)lists.aisnet.org

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Fwd: [AISWorld] IEEE BCCA 2021: The 3rd IEEE Conference on Blockchain Computing and Applications
by Gustaf Neumann 28 Jun '21

28 Jun '21

-------- Forwarded Message -------- Subject: [AISWorld] IEEE BCCA 2021: The 3rd IEEE Conference on Blockchain Computing and Applications Date: Mon, 28 Jun 2021 20:49:56 +0300 From: Müge Erel-Özçevik <mugeerelozcevik(a)gmail.com> To: aisworld(a)lists.aisnet.org *The Third IEEE Conference on Blockchain Computing and Applications (BCCA 2021)* *NOV. 15 – 17, 2021 – TARTU, ESTONIA* http://intelligenttech.org/BCCA2021/index.php Blockchain is a revolutionary technology in decentralized systems that enables secure decentralized transaction processing while ensuring data privacy and authenticity. It is now playing a significant role in several areas such the Internet of Things, supply-chain management, manufacturing, cyber-physical systems, healthcare systems, and much more. Unlike centralized transaction processing solutions, blockchain uses a distributed ledger mechanism to record data transactions on multiple devices, this will prevent data breach, identity theft, and a plethora of cyber-related attacks, in essence, leading to a sustainability in data privacy and security. This conference aims at to attract work of both researchers and practitioners in the area of cyber-security to share and exchange their experiences and research studies in both academia and industry in the field of blockchain. Researchers are encouraged to submit original research contributions in all major areas, which include, but not limited to: · *Track 01: Artificial Intelligence and Machine Learning* o Blockchain based artificial Intelligent Systems applications in Computers and Communications o Blockchain based AI and Robotics Technologies o Blockchain based AI and cloud computing o Blockchain based Economic paradigms and game theory o Blockchain based Machine and Deep Learning of Knowledge o Blockchain based Distributed Knowledge and Processing o Blockchain based Humans-Agents Interactions / Human-Robot Interactions · *Track 02: IoT and Cyber-Physical Systems* o Blockchain-based IoT Applications and Services o Blockchain-based security for the Internet of Things and cyber-physical systems o Blockchain-based Internet of Things architectures and protocols o Blockchain in Cyber Physical Systems (CPS) o Blockchain-based application in Intelligent Manufacturing: Industrial Internet of Things, o Blockchain and Secure Critical Infrastructure with Industry 4.0 o Intelligent manufacture and management o Consensus and mining algorithms suited for resource-limited IoTs o Blockchain-based Controlled mobility and QoS o Blockchain-based energy optimization techniques in WSN o Blockchain-based Software defined networks · *Track 03: Big Data* o Blockchain in Data Fusion o Blockchain Analytics and Data mining o Distributed data store for blockchain o Distributed transaction for blockchain o Blockchain based Data Science and Data Engineering o Protocols for management and access using blockchains o Blockchain architectures tailored for domain-specific applications · *Track 04: Security and Privacy on the Blockchain* o Authentication and authorization in Blockchain o Applications of blockchain technologies in digital forensic o Privacy aspects of blockchain technologies o Blockchain-based threat intelligence and threat analytics techniques o Blockchain-based open-source tools o Forensics readiness of blockchain technologies o Blockchain Attacks on Existing Systems o Blockchain Consensus Algorithms o Blockchain-based Intrusion Detection/Prevention o Security and Privacy in Blockchain and Critical Infrastructure o Attacks on Blockchain and Critical Infrastructure o Blockchain and Secure Critical Infrastructure with Smart Grid · *Track 05: Blockchain Research & Applications for Innovative Networks and Services* o State-of-the-art of the Blockchain technology and cybersecurity o Blockchain-based security solutions of smart cities infrastructures o Blockchain in connected and autonomous vehicles (CAV) and ITS) o Blockchain Technologies and Methodologies o Recent development and emerging trends Blockchain o New models, practical solutions and technological advances related to Blockchain o Theory of Blockchain in Cybersecurity o Applications of blockchain technologies in computer & hardware security o Implementation challenges facing blockchain technologies o Blockchain in social networking o Performance metric design, modeling and evaluation of blockchain systems o Network and computing optimization in blockchains o Experimental prototyping and testbeds for blockchains All accepted papers in BCCA 2021 will be submitted to IEEEXplore, dblp and Scopus for inclusion. *General Co-Chair* · Salil Kanhere, UNSW Sydney, Australia · Öznur Özkasap, Koç University, Turkey *Program Co-Chairs* · Moayad Aloqaily, xAnalytics Inc., Canada · Önder Gürcan, CEA LIST, Paris-Saclay University, France · Hong-Ning Dai, Macau University of Science and Technology, Macau *Keynote speakers:* · Giancarlo Fortino, Università della Calabria, Italy * Submission Link: *https://easychair.org/conferences/?conf=bcca2021 *Full paper Important Dates:* Submission deadline: *July 1st, 2021* Notification of Acceptance: Sept 15th, 2021 Submission of camera-ready: Oct 8th, 2021 _______________________________________________ AISWorld mailing list AISWorld(a)lists.aisnet.org

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Fwd: [AISWorld] Call for book chapters: Handbook of Teaching and Learning in Information Systems
by Gustaf Neumann 28 Jun '21

28 Jun '21

-------- Forwarded Message -------- Subject: [AISWorld] Call for book chapters: Handbook of Teaching and Learning in Information Systems Date: Mon, 28 Jun 2021 14:14:48 -0400 From: Mark Hwang <mark.hwang(a)cmich.edu> To: aisworld(a)lists.aisnet.org Dear Colleagues, The goal of this project is to provide a comprehensive, one-stop book for researchers interested in advancing IS education and IS pedagogical research. Chapters should be between 8,000 and 10,000 words (each figure counts as 500 words and each table as 300 words) and contain new and original research. Any research type is welcome: quantitative or qualitative; primary or secondary. The tentative deadline is June 30, 2022, with an expected publication date of June 30, 2023. The *Handbook* will be included in the Book Citation Index (part of the Web of Science) and the SCOPUS citation index. See this link <https://www.e-elgar.com/author-hub/handbooks/> for seven more reasons to contribute a chapter to an Elgar handbook. Please follow this link <https://1drv.ms/w/s!ApSj8gjXHwd4goYMA51M_I7WRNGfrg?e=q1PWV6> for an overview of the book’s structure. Issues related to IS education will be organized into seven themes or sections. Each section is further divided into chapters that deal with different issues or topics. These are suggested topics, and you are welcome to pick a related topic for your chapter. There are no submission or publication fees. Every chapter will be peer-reviewed to ensure the quality of the volume. Each author shall review no more than one chapter. If you are interested in participating, please send me a short proposal in about 150 words. Best, Mark Hwang Professor, Information Systems Central Michigan University mark.hwang(a)cmich.edu _______________________________________________ AISWorld mailing list AISWorld(a)lists.aisnet.org

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Fwd: [AISWorld] CfP "Trust in AI for Electronic Markets", Electronic Markets Journal
by Gustaf Neumann 28 Jun '21

28 Jun '21

-------- Forwarded Message -------- Subject: [AISWorld] CfP "Trust in AI for Electronic Markets", Electronic Markets Journal Date: Mon, 28 Jun 2021 12:52:33 +0200 From: Wolfgang Maass <wolfgang.maass(a)dfki.de> To: aisworld(a)lists.aisnet.org --- Apologies for cross-postings--- Dear colleagues, *Electronic Markets* is seeking submissions for a *Special Issue on "Trust in AI for electronic markets"*. Please find further details below. Call for Papers: “Trust in AI for electronic markets” Submission deadline: *December, 15, 2021* Guest Editors o Wolfgang Maass, Saarland University and German Research Center for Artificial Intelligence (DFKI), Germany, wolfgang.maass(at)dfki.de o Roman Lukyanenko, HEC Montréal, Canada, roman.lukyanenko(at)hec.ca o Veda C. Storey, Georgia State University, USA, vstorey(a)gsu.edu Theme Electronic markets for trading physical, as well as digital, goods offer a wide variety of services based on Artificial Intelligence technologies, as smart market services. Smart market services generate recommendations and predictions using Artificial Intelligence (AI) technologies on data available and accessible in electronic markets. For instance, financial high-speed trading is only feasible by smart market services that autonomously execute transactions according to market signals based on AI models trained with big data. Electronic marketplaces, including Amazon and Alibaba, are using AI technologies to provide smart services to consumers, optimize logistics, analyze consumer behavior, and derive innovative product and service designs. Some business leaders even consider there to be major threats to society from sophisticated AI solutions, while using AI extensively for their own business. Because AI systems elude human understanding and scrutinization, trust in AI is crucial for the success of smart market services, as well as other AI or machine learning-based systems . Gaining trust in AI begins with transparency in the reviews of (a) data so that biases and gaps in knowledge of a domain are controlled, (b) AI models and objective functions, (c) model performance and (d) results generated by AI models for decision making. Trust becomes an important factor for overcoming uncertainty on AI-based recommendations in general and in electronic markets in particular. The quality of smart market services depends on shared understanding and conceptual models of data used for training AI models; data quality; the selection and training of appropriate models; and the embedding of models into smart market services. Providers of smart market services are required to build trust relationships with business and end customers based on limited possibilities for opening the “black boxes” of Artificial Intelligence systems due to increased complexity of machine learning models. Empirical studies on trust in AI indicate heterogenous results. Companies and end-users appreciate benefits and opportunities provided by smart market services. At the same time, concerns are raised with respect to privacy issues and biases of data, models and algorithms. Overly optimistic customers might become disappointed if smart market services do not deliver as expected. Proof of privacy leaks and biases might reinforce prejudices. Both may lead to decrease of trust in AI. Challenging research questions are to identify which methods, indicators and experiences have increasing effects on trust in AI. For instance, explainable AI is a technical means for opening “black boxes” of AI systems, generally, and smart market services, specifically. This special issue seeks contributions on trust in Artificial Intelligence in the context of electronic markets. Contributions that help to understand challenges from an economic, legal or technical perspective are invited. Central issues and topics Possible topics of submissions include, but are not limited to: o Trust behavior and AI o Mental models, conceptual models and AI models o Psychological and sociological factors for trust in AI o Human-centric design of smart market services o Explainable AI for smart market services o Threats for trust in AI o Frameworks for smart markets o Business and legal aspects influencing trust in AI o Relationships between trust and Business models with smart market services o Transparency of data, AI models and recommendations o Case studies on building trust in AI Submission: Electronic Markets is a Social Science Citation Index (SSCI)-listed journal (IF 2.981 in 2019) in the area of information systems. We encourage original contributions with a broad range of methodological approaches, including conceptual, qualitative and quantitative research. Please also consider position papers and case studies for this special issue. All papers should fit the journal scope (for more information, see www.electronicmarkets.org/about-em/scope/) and will undergo a double-blind peer-review process. Submissions must be made via the journal’s submission system and comply with the journal's formatting standards. The preferred average article length is approximately 8,000 words, excluding references. If you would like to discuss any aspect of this special issue, you may either contact the guest editors or the Editorial Office. Keywords: Trust, Interpretability, Mental Models, Conceptual Models, Explainable AI, Smart Market Services, Privacy, Fairness of Artificial Intelligence, Biases, Transparency Important deadline * Submission Deadline: December, 15, 2021 References Domingos, P. (2012). A few useful things to know about machine learning. Communications of the ACM, 55(10), 78-87. https://doi.org/10.1145/2347736.2347755. Dwivedi, Y. K. et al. (2019). Artificial intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International Journal of Information Management, 57, 101994, https://doi.org/10.1016/j.ijinfomgt.2019.08.002.https://www.sciencedirect.c… Jacovi, A., Marasovi, A., Miller, T., & Goldberg, Y. (2021). Formalizing trust in artificial intelligence: Prerequisites, causes and goals of human trust in ai. In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, pp. 624-635. https://doi.org/10.1145/3442188.3445923. Luo, X., Tong, S., Fang, Z., & Qu, Z. (2019). Frontiers: Machines vs. humans: The impact of artificial intelligence chatbot disclosure on customer purchases. Marketing Science, 38(6), 937-947. https://doi.org/10.1287/mksc.2019.1192. Maass, W., Parsons, J., Purao, S., Storey, V. C., & Woo, C. (2018). Data-driven meets theory-driven research in the era of big data: opportunities and challenges for information systems research. Journal of the Association for Information Systems, 19(12), 1. https://doi.org/10.17705/1jais.00526. Maass, W., Parsons, J., Purao, S., & Storey, V. C. (2021). Pairing conceptual modeling with machine learning. Data & Knowledge Engineering, forthcoming. Maass, W., Storey, V. C., & Lukyanenko, R. (2021). From mental models to machine learning models via conceptual models. In Exploring Modeling Methods for Systems Analysis and Development (EMMSAD 2021), Melbourne, Australia, pp. 1–8. https://doi.org/10.1007/978-3-030-79186-5_19. Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). "Why should I trust you?" Explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135-1144. dx.doi.org/10.1145/2939672.2939778. Siau, K., & Wang, W. (2018). Building trust in artificial intelligence, machine learning, and robotics. Cutter Business Technology Journal, 31(2), 47-53. Thiebes, S., Lins, S., & Sunyaev, A. Trustworthy artificial intelligence. Electronic Markets, 31(2021)2. https://doi.org/10.1007/s12525-020-00441-4. Best regards, Rainer Alt, Hans-Dieter Zimmermann, Ramona Coia ==================================================================== Electronic Markets - The International Journal on Networked Business Editors-in-Chief: Rainer Alt, Leipzig University and Hans-Dieter Zimmermann, FHS St.Gallen, University of Applied Sciences Executive Editor: Ramona Coia, Leipzig University Editorial Office: c/o Information Systems Institute Leipzig University 04109 Leipzig, Germany Mail: editors(a)electronicmarkets.org Phone: +49-341-9733600 http://www.electronicmarkets.org https://www.facebook.com/ElectronicMarkets https://twitter.com/journal_EM https://www.springer.com/journal/12525 Journal Impact Factor 2019: 2.981 ——— Univ.-Prof. Dr.-Ing. Wolfgang Maaß German Research Center for Artificial Intelligence (DFKI) Saarland Informatics Campus A5 4 66123 Saarbrücken, Germany Phone: +49(0)681 302 64736 e-mail: wolfgang.maass(a)dfki.de http://www.dfki.de/web/research/sse http://maasslab.de _______________________________________________ AISWorld mailing list AISWorld(a)lists.aisnet.org

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Fwd: [AISWorld] Special issue On Data-driven Analytics in the Developments of a Smart City
by Gustaf Neumann 28 Jun '21

28 Jun '21

-------- Forwarded Message -------- Subject: [AISWorld] Special issue On Data-driven Analytics in the Developments of a Smart City Date: Mon, 28 Jun 2021 19:40:22 +0800 From: Yue Guo <yueggcn(a)aliyun.com> Reply-To: Yue Guo <yueggcn(a)aliyun.com> To: aisworld <aisworld(a)lists.aisnet.org> Special issue On Data-driven Analytics in the Developments of a Smart City https://www.emeraldgrouppublishing.com/journal/imds/data-driven-analytics-d… Please note: Submissions for this special issue will open on 21st September 2021. Smart cities define an emerging paradigm containing heterogeneous network infrastructure, ubiquitous sensor devices, big data processing, and intelligent control systems. Their primary purpose is to improve the quality of life of the citizens by providing intelligent services in a wide variety of aspects like transportation, environment, energy, etc. Big data analytics and operations research play an important role in enabling and delivering such intelligent services. Therefore, understanding how to use big data analytics and optimization for the operation and management of a smart city from different aspects, such as the allocation of power stations for electric taxis, charging batteries for car sharing, renewable power storage facilities for metros, and the charge/discharge management for all power storage facilities has aroused widespread concern in both academia and practice. Solving smart city issues can be costly and complicated and requires us to develop novel research approaches based on empirical data to provide more appropriate solutions to build smart city plans. The purpose of this special issue is to publish insights and viewpoints from scholars regarding solutions to smart city issues and challenges from different aspects, including the resource allocation and optimization of power stations, the location and capacity for shared car parking space, transportation strategy, and operation management of car sharing. Some topics of interest may include but are not limited to: Rigorous case studies on using data analytics for smart city development issues Data analytics for improving smart city operations efficiency Data analytics for car-sharing demand prediction Data analytics for transportation security The impact of smart cities on social welfare The application of data analytics to predict traffic congestion Using smart city visibility data for resource allocation Security-driven supply chain innovation The charge and discharge management of energy storage facilities such as subway capacitors, shared car batteries, and electric taxi batteries Submission and review process Please prepare the manuscript according to the journal’s “Guide for Authors and submission guidelines”. Please clearly state in the cover letter that the submission is made for this special issue. The review process will follow the journal’s double-blind practice. More information on the author guidelines can be found here. Prospective authors should submit an electronic copy of their complete manuscript via ScholarOne by selecting the article type “Data-Driven Analytics in the Development of a Smart City” in the online submission system here. Publication Schedule Submissions Open: September 21, 2021 Manuscript Submission Deadline: January 11, 2022 Final Decision Date: September 7, 2022 Expected Publication (Tentative): Autumn of 2022 Guest Editors Prof. Yue Guo Southern University of Science and Technology, China guoy(a)sustech.edu.cn Prof. Lean Yu University of Chinese Academy of Sciences, China yulean(a)amss.ac.cn Prof. Leandro C. Coelho Université Laval, Canada leandro.coelho(a)fsa.ulaval.ca Prof. Yichuan Ding McGill University, Canada daniel.ding(a)mcgill.ca _______________________________________________ AISWorld mailing list AISWorld(a)lists.aisnet.org

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Fwd: [AISWorld] CALL FOR PAPERS - EAIoT’2021 - with updated deadlines
by Gustaf Neumann 28 Jun '21

28 Jun '21

-------- Forwarded Message -------- Subject: [AISWorld] CALL FOR PAPERS - EAIoT’2021 - with updated deadlines Date: Mon, 28 Jun 2021 20:23:50 +0800 (GMT+08:00) From: 曹凯 <19083900110001(a)hainanu.edu.cn> To: aisworld(a)lists.aisnet.org First International Workshop on Latest Advances in Enterprise Architectures in the IoT Era (EAIoT’2021) http://eaiot2021.connect.rs In conjunction with IEEE EDOC’21 http://ieee-edoc.org/2021/ Gold Coast, Australia, October 25, 2021 CALL FOR PAPERS Internet of Things (IoT), one of the fastest growing Information and Communication Technologies (ICT), is impacting organizations from all perspectives (e.g., operational, legal, financial, and competitiveness) forcing them to review their functional and non-functional practices. According to the International Data Corporation (IDC), ``IoT spending will increase by a compound annual growth rate (CAGR) of 13.6% from 2017 to 2022, reaching $1.2 trillion within the next four years. It is also predicted 41 billion IoT devices by 2027 and 70% of automobiles will be connected to the Internet by 2023. To tap into the endless benefits and uses of IoT, the design principles and foundations of organizations’ enterprise architectures are expected to adjust to ensure a smooth integration of IoT into these architectures’ foundations namely organization, business, information, application, and technology. Aiming at examining these foundations separately and then collectively, this workshop is an open forum for discussions between academics and industry partners about the latest advances and developments in the dynamic field of enterprise architecture in the IoT era. The workshop addresses the lack of techniques and guidelines that would enable enterprises to integrate IoT into the life cycle of designing, developing, and deploying enterprise architectures. This integration should lead to a new generation of enterprise architectures that would foster not only a deeper retrospect on the involved interactive digital resources inside the life cycle covering data collection, information analysis, knowledge reasoning and wisdom strategies, but also a better understanding of potential threats as well as the development of new ways of aligning business and ICT resources together to improve the competitiveness of the enterprise in the background of the Artificial Intelligence (AI) trend. Whilst, on the one hand, IoT enacts many opportunities that enterprises could tap into, there are also obstacles that could undermine these opportunities, on the other hand. Some of these obstacles are lack of standards that cover both enterprise architecture and IoT, security holes that potentially exist in IoT devices making them questionable in terms of trust, security, and privacy, IoT limitations like silo restriction, computational capabilities, lack of semantic technologies that should describe IoT in a machine-understandable manner, just to mention some. Topics of Interest This workshop constitutes an opportunity for researchers from both disciplines enterprise computing (with focus on enterprise architecture) and IoT technologies to discuss how enterprises could capitalize on these technologies so, that, a new generation of business processes could spread over the emergent network of IoT. Topics for discussions include, but are not limited to: · Standards for IoT-based enterprise architecture. · Enterprise architecture for Industry 4.0. · Data science for IoT-based enterprise architecture. · Semantic technologies for IoT-based enterprise architecture. · Agentification of IoT-based enterprise architecture. · Interoperability inside and between IoT-based enterprise architecture. · Guidelines and best practices for IoT-based enterprise architecture · Privacy, trust, and security of IoT-based enterprise architecture. · Context management and awareness for IoT-based enterprise architecture · Case studies related to IoT-based enterprise architecture. COMMITTEES Organizing committee * Ejub Kajan, State University of Novi Pazar, Novi Pazar, Serbia * Zakaria Maamar, Zayed University, Dubai, UAE * Yucong Duan, Hainan University, Haikou, China Program committee * Mohammad Asim, FAST NUCES, Pakistan * Saoussen Cheikhrouhou, University of Sfax, Tunis * Marco Cremaschi, Università di Milano-Bicocca, Italy * Christophe Cruz, Université de Bourgogne, France * Luca Davoli, University of Parma, Italy * Frank Dieter-Dorloff, University of Duisburg-Essen, Germany * Qiang Duan, Pennsylvania State University, USA * Abdelrahman Elfaki, University of Tabuk, Saudi Arabia * Rik Eshuis, Eindhoven University of Technology, The Netherlands * Noura Faci, University Lyon1, Lyon, France * Francisco Falcone, Public University of Navarra, Pamplona, Spain * Veit Jahns, University of Duisburg-Essen, Germany * Soraya Kouadri Mostefaoui, Open University, UK * In Lee, Western Illniois University, USA * Sylvain Lefebvre, Toyota, Japan * Nanjangud C. Narendra, Ericsson, India * Hien D. Nguyen, University of Information Technology, Vietnam * Alexander Norta, Tallinn University of Technology, Estonia * Guadalupe Ortiz-Bellot, Cadiz University, Spain * Pitaya Poompuang, Rajamangala University of Technology Thanyaburi, Thailand * Dragan Stojanovi?, University of Niš, Serbia * Yang Xu, Fudan University, China * Chen Yang, Gent University, Belgium * Joe Zhou, IBM, USA IMPORTANT DATES AND SUBMISSION Important Dates · Paper Submission: August 16, 2021 · Authors Notification: September 13, 2021 · Camera Ready submission: September 27, 2021 · Author registration: September 27, 2021 · Workshop: October 25, 2021 Submission format The workshop welcomes conceptual and technical submissions as well submissions summarizing real case-studies. Submission should follow IEEE Computer Society Conference Proceedings Formatting Guidelines (8-10 pages for full and 4-6 pages for short) and be submitted in PDF format using the online EasyChair submission system at https://easychair.org/conferences/?conf=eaiot2021. All submissions will be refereed by at least 3 members of the international program committee. Selection criteria will include relevance, significance, impact, originality, and technical soundness. Publication The Proceedings will be published by the IEEE Computer Society Press and be available through IEEE Xplore and the IEEE Digital Library. Special Issue The guest editors have arranged a special issue in Cluster Computing Journal (https://www.springer.com/journal/10586) after the workshop. An open call for papers along with some targeted invitations will be issued in due time REGISTRATION PROCEDURE At least one author of each accepted paper should register to the venue and present the paper. Further instructions will be available in due course. 学校地址：中国海南省海口市人民大道58号　邮编：570228 网站地址: www.hainanu.edu.cn Copyright @ 2019 hainan university

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Fwd: [wkwi] Call for Papers: WI2022 - Prototype Track
by Gustaf Neumann 28 Jun '21

28 Jun '21

-------- Forwarded Message -------- Subject: [wkwi] Call for Papers: WI2022 - Prototype Track Date: Mon, 28 Jun 2021 12:55:53 +0000 From: Zschech, Patrick <patrick.zschech(a)fau.de> Reply-To: Zschech, Patrick <patrick.zschech(a)fau.de> To: wkwi(a)listserv.dfn.de <wkwi(a)listserv.dfn.de> (Bitte entschuldigen Sie eventuelle Mehrfachzustellungen.) ** *Call for Papers für den Prototype Track auf der WI 2022 - 17. Internationale Tagung Wirtschaftsinformatik vom 21. - 23. Februar 2022 an der Friedrich-Alexander-Universität Erlangen-Nürnberg* www.wi22.de <http://www.wi22.de>** ** Wir laden Sie herzlich dazu ein, prototypische Implementierungen innovativer IT-Artefakte einzureichen. Der Prototype Track bietet die Möglichkeit, funktionsfähige IT-Artefakte unterschiedlichster Ausprägungsformen während der Konferenz live zu demonstrieren und mit Forschern und Praktikern zu diskutieren. Als Einreichungen akzeptieren wir Kurzbeiträge, die durch ein begleitendes Demonstrationsvideo ergänzt werden. Akzeptierte Beiträge werden in der AIS Electronic Library veröffentlicht und in einer eigenen „Prototype“ Session präsentiert. *Beitragsformate* Der Track bietet die Möglichkeit zur Einreichung zwei unterschiedlicher Beitragsformate: * *Prototype Excellence:*In diesem Format steht die Funktionalität der entwickelten IT-Artefakte im Vordergrund, einschließlich der exzellenten Vorgehensweise bei der Prototypgestaltung und -entwicklung. Die Autoren haben die Möglichkeit, Einblicke in lauffähige Implementierungen aus fortwährenden Forschungsarbeiten zu demonstrieren. Es wird erwartet, dass die Prototypen bereits einer ersten (vorläufigen) Evaluation unterzogen wurden, die jedoch noch nicht vollständig abgeschlossen sein muss. Autoren werden dahingehend ermutigt, erste Evaluationsergebnisse zu berichten und zu diskutieren. * *Prototype Innovation:*Beim zweiten Beitragsformat steht der Innovationsgrad des entwickelten Prototyps im Vordergrund, wobei die wissenschaftliche Stringenz beim Design und der Evaluation in den Hintergrund rückt. Die Autoren haben die Möglichkeit besonders innovative IT-Artefakte zu präsentieren, die sich durch ihre Neuartigkeit, kreative und künstlerische Ausgestaltung sowie potenzielle Wettbewerbsfähigkeit auszeichnen. Eine Evaluation des Prototyps muss in diesem Beitragsformat noch nicht zwingend stattgefunden haben. Stattdessen werden IT-Artefakte in sehr frühen Entwicklungsphasen begrüßt. *Mögliche Themenschwerpunkte* Prototypen können unterschiedlichste Aspekte und Themenfelder der Wirtschafsinformatik adressieren und verschiedensten Anwendungsbereichen zugeordnet sein. Exemplarische, aber nicht ausschließliche Themenschwerpunkt sind zum Beispiel: * Business Analytics, Entscheidungsunterstützung * Künstliche Intelligenz, Maschinelles Lernen * Human Computer Interaction, Chatbots, Assistenzsysteme * Virtual/Augmented/Mixed Reality * Internet of Things, Cyber Physical Systems * Robotic Process Automation * Gamification * Sicherheit und Datenschutz * Healthcare und Wellbeing * Energy Informatics, Green IT * Industrie 4.0/Dienstleistung 4.0/Arbeit 4.0 * Handel, Supply Chain Management, Logistik * Financial Technologies und Blockchain * Bildung, E-Learning ** *Track Chairs* * Patrick Zschech, Friedrich-Alexander-Universität Erlangen-Nürnberg * Oliver Müller, Universität Paderborn ** *Associate Editors* * Jürgen Anke (Hochschule für Technik und Wirtschaft Dresden) * Christian Bartelheimer (Universität Paderborn) * Christoph Flath (Julius-Maximilians-Universität Würzburg) * Dominik Gutt (Erasmus University Rotterdam) * Kai Heinrich (Otto-von-Guericke-Universität Magdeburg) * Konstantin Hopf (Universität Bamberg) * Christian Janiesch (Julius-Maximilians-Universität Würzburg) * Niklas Kühl (Karlsruher Institut für Technologie) * Stefan Morana (Universität des Saarlandes) * Dimitri Petrik (Universität Stuttgart) * Jana Rehse (Universität Mannheim) * Stefan Seidel (Universität Liechtenstein) * Jeannette Stark (Technische Universität Dresden) * Susanne Strahringer (Technische Universität Dresden) * Uwe Wieland (Hochschule für Technik und Wirtschaft Dresden) ** *Zeitplan für den Begutachtungsprozess* Die Zeitplanung richtet sich an den Terminen der regulären, wissenschaftlichen Tracks aus: * Letzte Möglichkeit für eine Einreichung (Submission Deadline): 01.09.2021 – 14 Uhr * Fast and Constructive AE Feedback: 10.09.2021 * Abgabe der Reviewer-Gutachten: 10.10.2021 * Abgabe der AE-Gutachten: 20.10.2021 * Entscheidung der Track-Chairs: 27.10.2021 * Information an Autoren: 31.10.2021 * Einreichung der überarbeiteten Beiträge: 15.11.2021 * Finale Entscheidung und Information an Autoren: 21.11.2021 Weiterführende Informationen sowie Details zum Einreichungsverfahren finden Sie unter https://www.wi22.de/tracks/prototypen/ Wir freuen uns auf Ihre #exzellenten und #innovativen Prototypen! ______________________________________ *Prof. Dr. Patrick Zschech* Friedrich-Alexander-Universität Erlangen-Nürnberg Fachbereich Wirtschafts- und Sozialwissenschaften Institut für Wirtschaftsinformatik / Intelligent Information Systems Lange Gasse 20, 90403 Nürnberg patrick.zschech(a)fau.de <mailto:patrick.zschech@fau.de> www.intelligentsystems.wiso.rw.fau.de/ <http://www.intelligentsystems.wiso.rw.fau.de/>

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Fwd: [AISWorld] Call for special issue papers - Intelligence Support for Mentoring Processes in Higher Education (and beyond)
by Gustaf Neumann 28 Jun '21

28 Jun '21

-------- Forwarded Message -------- Subject: [AISWorld] Call for special issue papers - Intelligence Support for Mentoring Processes in Higher Education (and beyond) Date: Mon, 28 Jun 2021 18:11:56 +0300 (EEST) From: Elvira Popescu <popescu_elvira(a)software.ucv.ro> To: aisworld(a)lists.aisnet.org Intelligence Support for Mentoring Processes in Higher Education (and beyond) Research Topic in Frontiers in Artificial Intelligence (AI for Human Learning and Behavior Change) https://www.frontiersin.org/research-topics/14009/intelligence-support-for-… Submission deadline: 30 September 2021 Mentoring is the activity of a senior person (the mentor) supporting a less experienced person (the mentee) in learning. It is based on a trustful, protected and private atmosphere between the mentor and the mentee. The goal is to develop a professional identity and to reflect the current situation. At universities, mentors are senior academics or skilled employees while mentees are mostly students with different competences. Outside universities, mentors and mentees are professionals. Intelligent tutoring systems have a long tradition, focusing on cognitive aspects of learning in a selected domain. They were successfully applied especially in such areas, where the domain knowledge can be well formalised with the help of experts. Nevertheless, in the learning process also motivations, emotions and meta-cognitive competences play a crucial role. These can be nowadays quite well recognised and monitored through big educational data and a wide spectrum of available sensors. This enables the support also for the mentoring process, which is more spontaneous, holistic and depends on the needs and interests of the mentee. Psychological and emotional support are at the heart of the mentoring relationship, underpinned by empathy and trust. Various roles and success factors for mentoring have been identified. We want to look at these aspects and investigate how they were technologically supported, in order to specify the requirements for intelligent mentoring systems. This should help us to answer the following questions: How can we design educational concepts that enable a scalable individual mentoring in the development of competences? How can we design intelligent mentoring systems to cover typical challenges and to scale up mentoring support in universities and outside? How can we design an infrastructure to exchange data between universities in a private and secure way to scale up on the inter-university level? How can we integrate heterogeneous data sources (learning management systems, sensors, social networking sites) to facilitate learning analytics supporting mentoring processes? Topics include but are not limited to: • Pedagogical models of mentoring • Peer mentoring & crowdsourcing mentoring • Workplace & career mentoring • Meta-cognitive competences of mentoring • Chatbots in Mentoring • Mixed Reality Mentoring • Wearables and Sensors for mentoring • Self-regulated mentoring, nudging & behaviour change • Mentoring analytics • Mentoring support in learning management systems • Mobile mentoring support • Design and research methodologies for mentoring support • Measuring and Analysing mentoring support • Visualization techniques for mentoring support • Motivation and gamification of mentoring support • Deep learning, machine learning and data mining in mentoring support • Recommender technologies for mentoring support (mentor-mentee matching) • Semantic technologies for mentoring support (ontologies, domain & mentoring models) • Distributed mentoring environments (cloud & p2p platforms) • Mentoring for specific domains & subjects (math, engineering, social sciences, pedagogy) • Affective computing for mentoring • Requirements of intelligent mentoring systems If you decide to submit a manuscript within our collection, your contribution will be peer-reviewed and judged on originality, interest, clarity, relevance, correctness, language, and presentation (inter alia) by our editorial board members. Immediately upon publication, your paper will be free to read for everyone, increasing visibility, and citations. We encourage authors to submit Abstracts ahead of the full manuscript submission. Topic Editors: • Ralf Klamma (RWTH Aachen University, Germany) • Milos Kravcik (German Research Center for Artificial Intelligence - DFKI, Germany) • Elvira Popescu (University of Craiova, Romania) • Viktoria Pammer-Schindler (Graz University of Technology, Austria) _______________________________________________ AISWorld mailing list AISWorld(a)lists.aisnet.org

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