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Given the heightened global awareness and attention to the negative externalities of plastics use, many state and local governments are considering legislation that will limit single-use plastics for consumers and retailers under extended producer responsibility laws. Considering the growing momentum of these single-use plastics regulations globally, there is a need for reliable and cost-effective measures of the public response to this rulemaking for inference and prediction. Automated computational approaches such as generative AI could enable real-time discovery of consumer preferences for regulations but have yet to see broad adoption in this domain due to concerns about evaluation costs and reliability across large-scale social data. In this study, we leveraged the zero and few-shot learning capabilities of GPT-4 to classify public sentiment towards regulations with increasing complexity in expert prompting. With a zero-shot approach, we achieved a 92% F1 score (s.d. 1%) and 91% accuracy (s.d. 1%), which resulted in three orders of magnitude lower research evaluation cost at 0.138 pennies per observation. We then use this model to analyze 5,132 tweets related to the policy process of the California SB-54 bill, which mandates user fees and limits plastic packaging. The policy study reveals a 12.4% increase in opposing public sentiment immediately after the bill was enacted with no significant changes earlier in the policy process. These findings shed light on the dynamics of public engagement with lower cost models for research evaluation. We find that public opposition to single-use plastics regulations becomes evident in social data only when a bill is effectively enacted. 

Given the heightened global awareness and attention to the negative externalities of plastics use, many state and local governments are considering legislation that will limit single-use plastics for consumers and retailers under extended producer responsibility laws. Considering the growing momentum of these climate regulations globally, there is a need for reliable and cost-effective measures of the public response to this rulemaking for inference and prediction. Automated computational approaches such as generative AI could enable real-time discovery of consumer preferences for regulations but have yet to see broad adoption in this domain due to concerns about evaluation costs and reliability across large-scale social data. In this study, we leveraged the zero and few-shot learning capabilities of GPT-4 to classify public sentiment towards regulations with increasing complexity in expert prompting. With a zero-shot approach, we achieved a 92% F1 score (s.d. 1%) and 91% accuracy (s.d. 1%), which resulted in three orders of magnitude lower research evaluation cost at 0.138 pennies per observation. We then use this model to analyze 5,132 tweets related to the policy process of the California SB-54 bill, which mandates user fees and limits plastic packaging. The policy study reveals a 12.4% increase in opposing public sentiment immediately after the bill was enacted with no significant changes earlier in the policy process. These findings shed light on the dynamics of public engagement with lower cost models for research evaluation. We find that public opposition to single-use plastics regulation becomes evident in social data only when a bill is effectively enacted. 

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.