First International Workshop on Requirements Engineering for Explainable Systems (RE4ES)

Authors: Mersedeh Sadeghi, Verena Klös and Andreas Vogelsang

Abstract: The need for systems to explain behavior to users has become more evident with the rise of complex technology like machine learning or self-adaptation. In general, the need for an explanation arises when the behavior of a system does not match the user’s expectation. However, there may be several reasons for a mismatch including errors, goal conflicts, or multi-agent interference. Given the various situations, we need precise and agreed descriptions of explanation needs as well as benchmarks to align research on explainable systems. In this paper, we present a taxonomy that structures needs for an explanation according to different reasons. For each leaf node in the taxonomy, we provide a scenario that describes a concrete situation in which a software system should provide an explanation. These scenarios, called explanation cases, illustrate the different demands for explanations. Our taxonomy can guide the requirements elicitation for explanation capabilities of interactive intelligent systems and our explanation cases build the basis for a common benchmark. We are convinced that both, the taxonomy and the explanation cases, help the community to align future research on explainable systems.

Authors: Wasja Brunotte, Larissa Chazette and Kai Korte

Abstract: Using systems as support tools for decision-making is a common part of a citizen’s daily life. Systems support users in various tasks, collecting and processing data to learn about a user and provide more tailor-made services. This data collection, however, means that users’ privacy sphere is increasingly at stake. Informing the user about what data is collected and how it is processed is key to reaching transparency, trustworthiness, and ethics in modern systems. While laws and regulations have come into existence to inform the user about privacy terms, this information is still conveyed in a complex and verbose way to the user, making it unintelligible to them. Meanwhile, explainability is seen as a way to disclose information about a system or its behavior in an intelligible manner. In this work, we propose explanations as a means to enhance users’ privacy awareness. As a long-term goal, we want to understand how to achieve more privacy awareness with respect to systems and develop heuristics that support it, helping end-users to protect their privacy. We present preliminary results on private sphere explanations and present our research agenda towards our long-term goal.

Authors: My Linh Nguyen, Thao Phung, Duong-Hai Ly and Hong-Linh Truong

Abstract: End-to-end machine learning (ML) in Internet of Things (IoT) Cloud system consists of multiple processes, covering data, model, and service engineering, and is involved by multiple stakeholders. Therefore, to be able to explain ML to relevant stakeholders, it is important to identify explainability requirements in a holistic manner. In this paper, we present our methodology to identify explainability requirements for end-to-end ML in developing ML services to be deployed within IoT Cloud systems. We identify and classify explainability requirements engineering through (i) involvement of relevant stakeholders, (ii) end-to-end data, model, and service engineering processes, and (iii) multiple explainability aspects. We present our work with a case of predictive maintenance for Base Transceiver Stations (BTS) in the telco domain.

Authors: Lena Kästner, Markus Langer, Veronika Lazar, Astrid Schomaecker, Timo Speith and Sarah Sterz

Abstract: Recently, requirements for the explainability of software systems have gained prominence. One of the primary motivators for such requirements is that explainability is expected to facilitate stakeholders' trust in a system. Although this seems intuitively appealing, recent psychological studies indicate that explanations do not necessarily facilitate trust. Thus, explainability requirements might not be suitable for promoting trust.

One way to accommodate this finding is, we suggest, to focus on trustworthiness instead of trust. While these two may come apart, we ideally want both: a trustworthy system and the stakeholder's trust. In this paper, we argue that even though trustworthiness does not automatically lead to trust, there are several reasons to engineer primarily for trustworthiness -- and that a system's explainability can crucially contribute to its trustworthiness.

Authors: Sarah Sterz, Kevin Baum, Anne Lauber-Rönsberg and Holger Hermanns

Abstract: System quality attributes like explainability, transparency, traceability, explicability, interpretability, understandability, and the like are given an increasing weight, both in research and in the industry. All of these attributes can be subsumed under the term of "perspicuity". We argue in this vision paper that perspicuity is to be regarded as a meaningful and distinct class of quality attributes from which new requirements along with new challenges arise, and that perspicuity as a requirement is needed for legal, societal, and moral reasons, as well as for reasons of consistency within requirements engineering.

Authors: Maike Schwammberger

Abstract: While autonomous systems are increasingly capturing the markets, they also become more and more complex. Thus, the (self-) explainability of these complex and adaptive systems becomes evermore important. We introduce explainability to our previous work on formally proving properties of autonomous urban traffic manoeuvres. We build causal diagrams by connecting actions of a crossing protocol with their reasons and derive explanation paths from these diagrams. We strive to bring our formal methods approach together with requirements engineering approaches, by suggesting to use run-time requirements engineering to update our causal diagrams at run-time.