In the last few years, AI continues demonstrating its positive impact on society while sometimes with ethically questionable consequences. Building and maintaining public trust in AI has been identified as the key to successful and sustainable innova
tion. This chapter discusses the challenges related to operationalizing ethical AI principles and presents an integrated view that covers high-level ethical AI principles, the general notion of trust/trustworthiness, and product/process support in the context of responsible AI, which helps improve both trust and trustworthiness of AI for a wider set of stakeholders.
Blockchain has been increasingly used as a software component to enable decentralisation in software architecture for a variety of applications. Blockchain governance has received considerable attention to ensure the safe and appropriate use and evol
ution of blockchain, especially after the Ethereum DAO attack in 2016. To understand the state-of-the-art of blockchain governance and provide an actionable guidance for academia and practitioners, in this paper, we conduct a systematic literature review, identifying 34 primary studies. Our study comprehensively investigates blockchain governance via 5W1H questions. The study results reveal several major findings: 1) the adaptation and upgrade of blockchain are the primary purposes of blockchain governance, while both software quality attributes and human value attributes need to be increasingly considered; 2) blockchain governance mainly relies on the project team, node operators, and users of a blockchain platform; and 3) existing governance solutions can be classified into process mechanisms and product mechanisms, which mainly focus on the operation phase over the blockchain platform layer.
Federated learning has received fast-growing interests from academia and industry to tackle the challenges of data hungriness and privacy in machine learning. A federated learning system can be viewed as a large-scale distributed system with differen
t components and stakeholders as numerous client devices participate in federated learning. Designing a federated learning system requires software system design thinking apart from machine learning knowledge. Although much effort has been put into federated learning from the machine learning technique aspects, the software architecture design concerns in building federated learning systems have been largely ignored. Therefore, in this paper, we present a collection of architectural patterns to deal with the design challenges of federated learning systems. Architectural patterns present reusable solutions to a commonly occurring problem within a given context during software architecture design. The presented patterns are based on the results of a systematic literature review and include three client management patterns, four model management patterns, three model training patterns, and four model aggregation patterns. The patterns are associated to the particular state transitions in a federated learning model lifecycle, serving as a guidance for effective use of the patterns in the design of federated learning systems.
