Greywater recycling: Treatment options and applications

Wastewater is an immense resource that could find significant applications in regions of water scarcity. Greywater has particular advantages in that it is a large source with a low organic content. Through critical analysis of data from existing greywater recycling applications, this paper presents a review of existing technologies and applications by collating a disparate information base and comparing/contrasting the strengths and weaknesses of different approaches. Simple technologies and sand filters have been shown to have a limited effect on greywater; membranes are reported to provide good solids removal but cannot efficiently tackle the organic fraction. Alternatively, biological and extensive schemes achieve a good general treatment of greywater with particularly effective removal of organics. The best overall performances were observed within schemes that combine different types of methods to ensure effective treatment of all the fractions

Scenario-based requirements elicitation for user-centric explainable AI

Explainable Artificial Intelligence (XAI) develops technical explanation methods and enable interpretability for human stakeholders on why Artificial Intelligence (AI) and machine learning (ML) models provide certain predictions. However, the trust of those stakeholders into AI models and explanations is still an issue, especially domain experts, who are knowledgeable about their domain but not AI inner workings. Social and user-centric XAI research states it is essential to understand the stakeholder’s requirements to provide explanations tailored to their needs, and enhance their trust in working with AI models. Scenario-based design and requirements elicitation can help bridge the gap between social and operational aspects of a stakeholder early before the adoption of information systems and identify its real problem and practices generating user requirements. Nevertheless, it is still rarely explored the adoption of scenarios in XAI, especially in the domain of fraud detection to supporting experts who are about to work with AI models. We demonstrate the usage of scenario-based requirements elicitation for XAI in a fraud detection context, and develop scenarios derived with experts in banking fraud. We discuss how those scenarios can be adopted to identify user or expert requirements for appropriate explanations in his daily operations and to make decisions on reviewing fraudulent cases in banking. The generalizability of the scenarios for further adoption is validated through a systematic literature review in domains of XAI and visual analytics for fraud detection