Artificial intelligence (AI) systems have many applications with tremendous current and future value to human society. As AI systems penetrate the aspects of everyday life, a pressing need arises to explain their decision-making processes to build trust and familiarity among end users. In high-stakes fields such as healthcare and self-driving cars, AI systems are required to have a minimum standard for accuracy and to provide well-designed explanations for their output, especially when they impact human life. Although many techniques have been developed to make algorithms explainable in human terms, no design methodologies that will allow software teams to systematically draw out and address explainability-related issues during AI design and conception have been established. In response to this gap, we proposed the explainability in design (EID) methodological framework for addressing explainability problems in AI systems. We explored the literature on AI explainability to narrow down the field into six major explainability principles that will aid designers in brainstorming around the metrics and guide the critical thinking process. EID is a step-by-step guide to AI design that has been refined over a series of user studies and interviews with experts in AI explainability. It is devised for software design teams to uncover and resolve potential issues in their AI products and to simply refine and explore the explainability of their products and systems. The EID methodology is a novel framework that aids in the design and conception stages of the AI pipeline and can be integrated into the form of a step-by-step card game. Empirical studies involving AI system designers have shown that EID can decrease the barrier of entry and the time and experience required to effectively make well-informed decisions for integrating explainability into their AI solutions.

As Artificial Intelligence (AI) and Digital Transformation (DT) technologies become increasingly ubiquitous in modern society, the flaws in their designs are starting to attract attention. AI models have been shown to be susceptible to biases in the training data, especially against underrepresented groups. Although an increasing call for AI solution designers to take fairness into account, the field lacks a design methodology to help AI design teams of members from different backgrounds brainstorm and surface potential fairness issues during the design stage. To address this problem, we propose the Fairness in Design (FID) framework to help AI software designers surface and explore complex fairness-related issues, that otherwise can be overlooked. We explore literature in the field of fairness in AI to narrow down the field into ten major fairness principles, which assist designers in brainstorming around metrics and guide thinking processes about fairness. FID facilitates discussions among design team members, through a game-like approach that is based on a set of prompt cards, to identify and discuss potential concerns from the perspective of various stakeholders. Extensive user studies show that FID is effective at assisting participants in making better decisions about fairness, especially complex issues that involve algorithmic decisions. It has also been found to decrease the barrier of entry for software teams, in terms of the pre-requisite knowledge about fairness, to address fairness issues so that they can make more appropriate related design decisions. The FID methodological framework contributes a novel toolkit to aid in the design and conception process of AI systems, decrease barriers to entry, and assist critical thinking around complex issues surrounding algorithmic systems. The framework is integrated into a step-by-step card game for AI system designers to employ during the design and conception stage of the life-cycle process. FID is a unique decision support framework for software teams interested to create fairness-aware AI solutions.