Design

Multi-level co-architecturing

Multi-level co-architecting is required to ensure the seamless integration of different components, including co-architecting AI components and non-AI components and co-architecting of different AI model pipeline components. Multilevel co-architecting allows both system- and model-level requirements to be considered in design decision making.

Envisioning card

Envisioning cards [17, 115] are designed to help the development team operationalize human values during design processes of AI systems. The design of envisioning cards is based on four envisioning criteria, including stakeholder, time, value, and pervasiveness. The stakeholder criterion helps the development team takes into account the effects of an AI system on both direct stakeholders and indirect stakeholders. The time criterion emphasizes the long-term implication of AI systems on human, society, and environments. The value criterion guides the development team to consider the impact of AI systems on human values. The pervasiveness criterion discusses the challenges encountered if an AI system is widely adopted in terms of geography, culture, demographics, and so on.

Design modeling for ethics

Design modeling methods can be extended and used to support the modeling of AI components and the ethical aspects, including using UML to describe the architecture of AI systems and represent their ethical aspects [114], designing formal models taking into account human values [36], using ontologies to model the AI system artifacts for accountability [10, 85], establishing RAI knowledge bases for making design decisions considering ethical concerns [101], and using logic programming to implement ethical principles

System-Level Ethical Simulation

System-level simulation (e.g., [29, 30, 96, 106]) is a cost-effective way to imitate real-world situations and assess the behaviors of AI systems before deploying AI systems in the real world. A simulation model needs to be built to mimic the possible behaviors and decisions of the AI system and assess the ethical impacts. The assessment results can be sent to the development team or potential users before the AI systems are deployed in the real world. System-level simulation can predict potential ethical risks and avoid serious ethical disasters before deploying AI systems in the real world.

Human-Centered Interface Design for Explainable Artificial Intelligence

Explainable Artificial Intelligence (XAI) can be viewed as a human-AI interaction problem and achieved by effective human-centered interface design. Checklists (e.g., a question bank) are often used to help design the explainable user interfaces [62, 66, 67] and understand the user needs, choices of XAI techniques, and XAI design factors [67]. For example, the checklist questions could consider the following aspects [66] for different stakeholders: input, output, how, performance (can be extended to ethical performance), why and why not, what if, and so on.