Call for Papers

CfP special issue on "Explainable and responsible artificial intelligence"

No longer accepting submissions


Guest editors


Today’s algorithms already reached or even surpassed the task performance of humans in various domains. Especially, AI plays a central role for the interaction between organizations and individuals such as their customers, transforming for instance electronic commerce or customer relationship management. However, most AI systems are still “black boxes” that are difficult to comprehend—not only for developers, but also for consumers and decision-makers (Meske, Bunde, Schneider and Gersch 2020). With regards to electronic markets, problems such as trying to manage the risk and ensure regulatory compliance of electronic trading systems based on machine learning stem not only from their data-driven nature and technical complexity, but also from their black-box nature, where the “learning” creates non-transparent dependencies between inputs and outputs (Cliff and Treleaven 2010). This raises many challenges such as ensuring data quality issues, managing provenance information needed for transparency as well as organizing metadata when combining data from multiple sources (Rabhi, Mehandjiev and Baghdadi 2020). Thus, a responsible and more trustworthy AI is demanded (HLEG-AI 2019; Thiebes, Lins and Sunyaev 2020).

This is where research on Explainable Artificial Intelligence (XAI) comes in. Also referred to as “interpretable”, “responsible”, or “understandable AI”, XAI aims to “produce explainable models, while maintaining a high level of learning performance (prediction accuracy); and enable human users to understand, appropriately, trust, and effectively manage the emerging generation of artificially intelligent partners” (DARPA 2017). XAI hence refers to “the movement, initiatives, and efforts made in response to AI transparency and trust concerns, more than to a formal technical concept” (Adadi and Berrada 2018, p. 52140). XAI is designed user-centric in that users are empowered to scrutinize AI (Förster, Klier, Kluge and Sigler 2020). Overall, XAI supports to evaluate, to improve, to learn from, and to justify AI, in order to eventually be able to manage AI (Meske, Bunde, Schneider and Gersch 2020).

With a focus on the transformation of electronic markets, in this special issue, we intend to explore and extend research on how to establish explainability and responsibility in intelligent black box systems—machine learning-based or not. On that account, we invite researchers to submit their papers from all application domains, such as e-commerce, customer relationship management, healthcare, finance, retail, public administration or others.

Central issues and topics

This special issue of the Electronic Markets Journal will focus on new, innovative approaches to explainable and responsible AI systems that will change/improve the interaction between organizations and individuals. They should discuss how their approaches and solutions enable enhanced ways of information exchange, decision-making, and service science. Technically and method-oriented studies, case studies as well as design science or behavioral science approaches are welcome.

This special issue is not only intended for academics and researchers but will also be valuable for executives, managers, innovators and project leaders who would like to implement explainable and responsible AI systems. The (non-exclusive) list of topics includes:

  • Designing and deploying XAI systems in electronic markets
  • XAI to foster trust in AI-based buyer-seller interactions (e.g., chatbots, recommender systems)
  • Addressing user-centric requirements for XAI systems
  • Addressing the responsibility of AI systems
  • Explainability as a prerequisite for responsible AI systems
  • Impact of explainability on AI-based digital platform use and adoption
  • Prevention and detection of deceptive AI explanations
  • XAI to discover deep knowledge and learn from AI
  • Presentation and personalization of AI explanations for different target groups
  • XAI to increase situational awareness and compliance behavior
  • XAI for transparency and unbiased decision making
  • Potential harm of explainability in AI
  • Explainability and responsibility policy guidelines
  • XAI and ethics


Explainable Artificial Intelligence, Responsible Artificial Intelligence, Explainability, Transparency, Managing AI


Electronic Markets is a Social Science Citation Index (SSCI)-listed journal (IF 4.765 in 2020) 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 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.


Adadi A., Berrada M. (2018). Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI). IEEE Access 6:52138-52160.

Cliff D. and Treleaven, P. (2010). Technology trends in the financial markets: A 2020 vision, UK Government Office for Science’s Foresight Driver Review on The Future of Computer Trading in Financial Markets – DR 3, October 2010

Defense Advanced Research Projects Agency (DARPA) (2017). Explainable Artificial Intelligence (XAI). Accessed 7 April 2021.

Förster, M., Klier, M., Kluge, K. and Sigler, I. (2020). Fostering Human Agency: A Process for the Design of User-Centric XAI Systems. Proceedings of the 41th International Conference on Information Systems (ICIS).

HLEG-AI. (2019). Ethics Guidelines for Trustworthy Artificial Intelligence. Brussels: Independent High-Level Expert Group on Artificial Intelligence set up by the European Commission.

Meske, C., Bunde, E., Schneider, J. and Gersch, M. (2020). Explainable Artificial Intelligence: Objectives, Stakeholders and Future Research Opportunities. Information Systems Management (ISM), p. 1-11, doi:

Rabhi, F. A., Mehandjiev, N. and Baghdadi, A. (2020). State-of-the-Art in Applying Machine Learning to Electronic Trading. In International Workshop on Enterprise Applications, Markets and Services in the Finance Industry (pp. 3-20). Springer Lecture Notes in Business Information Processing.

Thiebes, S., Lins, S. and Sunyaev, A. (2020). Trustworthy artificial intelligence. Electronic Markets (EM), p. 1-18, doi: