ABSTRACT
Humans have been exploiting the environmental resources as much as the environment today stands in crisis. There has become an urgent need of aligning profits with peace and prosperity of people and planet. In the race of economic growth and development, the disparity between social, economic and environment has aroused. The impact of Covid-19 pandemic further has highlighted the sustainable growth to be considered for a better future. Also, viewing the United Nations Sustainable Development Goals, there needs to be responsible consumption and production. Sustainability is the greatest challenge faced by the apparel industry. The industry, running on the adrenaline of glamor, pricing and pace, has recently realized that the old systems and processes cannot sustain, but there are not enough new systems and processes to replace. We are facing redundancies in an empty-handed way. Green manufacturing is an inevitable future. The paper explores key aspects of green manufacturing from apparel industries perspective, noting down the innovation and entrepreneurial solutions to the problems, identifying gaps and future scope. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
ABSTRACT
This paper presents a user-centered approach to translating techniques and insights from AI explainability research to developing effective explanations of complex issues in other fields, on the example of COVID-19. We show how the problem of AI explainability and the explainability problem in the COVID-19 pandemic are related: as two specific instances of a more general explainability problem, occurring when people face in-transparent, complex systems and processes whose functioning is not readily observable and understandable to them ("black boxes”). Accordingly, we discuss how we applied an interdisciplinary, user-centered approach based on Design Thinking to develop a prototype of a user-centered explanation for a complex issue regarding people's perception of COVID-19 vaccine development. The developed prototype demonstrates how AI explainability techniques can be adapted and integrated with methods from communication science, visualization and HCI to be applied to this context. We also discuss results from a first evaluation in a user study with 88 participants and outline future work. The results indicate that it is possible to effectively apply methods and insights from explainable AI to explainability problems in other fields and support the suitability of our conceptual framework to inform that. In addition, we show how the lessons learned in the process provide new insights for informing further work on user-centered approaches to explainable AI itself. © 2022, The Author(s).