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Abstract Recent calls have been made for equity tools and frameworks to be integrated throughout the research and design life cycle —from conception to implementation—with an emphasis on reducing inequity in artificial intelligence (AI) and machine learning (ML) applications. Simply stating that equity should be integrated throughout, however, leaves much to be desired as industrial ecology (IE) researchers, practitioners, and decision‐makers attempt to employ equitable practices. In this forum piece, we use a critical review approach to explain how socioecological inequities emerge in ML applications across their life cycle stages by leveraging the food system. We exemplify the use of a comprehensive questionnaire to delineate unfair ML bias across data bias, algorithmic bias, and selection and deployment bias categories. Finally, we provide consolidated guidance and tailored strategies to help address AI/ML unfair bias and inequity in IE applications. Specifically, the guidance and tools help to address sensitivity, reliability, and uncertainty challenges. There is also discussion on how bias and inequity in AI/ML affect other IE research and design domains, besides the food system—such as living labs and circularity. We conclude with an explanation of the future directions IE should take to address unfair bias and inequity in AI/ML. Last, we call for systemic equity to be embedded throughout IE applications to fundamentally understand domain‐specific socioecological inequities, identify potential unfairness in ML, and select mitigation strategies in a manner that translates across different research domains. 

The built environment requires extraction and consumption of enormous quantities of raw materials, water, and energy. While these materials remain in use for several years or decades, growing global populations and aging infrastructure are driving widespread generation of one of the largest and most challenging waste streams to manage. There is growing interest from communities in integrating circular economy (CE) strategies in the context of construction & demolition (C&D) material management. Many approaches for doing so focus on small-scale CE applications like individual products, materials, or projects. However, greater understanding is needed at the city-scale given communities’ complex position at the frontlines of local development, resource consumption, and waste management. This study summarizes the development of an evaluative framework for community-based C&D circularity at a city or regional level. The framework expands upon a mixed methods approach called the Circularity Assessment Protocol (CAP), which integrates aspects of urban metabolism, geospatial analysis, and qualitative research methods to examine plastic waste management in communities. To advance convergent CE research, here, we aim to adapt the CAP framework to C&D. We describe our adaptation of the CAP to C&D through a conceptual review describing research, methods, and strategies related to seven elements of a local CE context: C&D Analytics, Building Material and Design, Community, Use, Collection, End-of-Cycle, and C&D Emissions. This work describes a novel yet preliminary conceptualization for developing a baseline understanding of circular C&D material management and a holistic examination of barriers, affordances, and opportunities for improving city-wide circularity.