{"type":"ScholarlyArticle","title":"SUSTAINABLESIGNALS: An AI Approach for Inferring Consumer Product Sustainability","authors":[{"name":"Tong Lin","slug":"tong-lin","given":"Tong","family":"Lin","affiliation":"School of Information , University of Michigan"},{"name":"Tianliang Xu","slug":"tianliang-xu","given":"Tianliang","family":"Xu","affiliation":"School of Information , University of Michigan"},{"name":"Amit Zac","slug":"amit-zac","given":"Amit","family":"Zac","affiliation":"Center for Law and Economics , ETH Zurich; Law Faculty , University of Amsterdam"},{"name":"Sabina Tomkins","slug":"sabina-tomkins","given":"Sabina","family":"Tomkins","affiliation":"School of Information , University of Michigan"}],"abstract":"The everyday consumption of household goods is a significant source of environmental pollution. The increase of online shopping affords an opportunity to provide consumers with actionable feedback on the social and environmental impact of potential purchases, at the exact moment when it is relevant. Unfortunately, consumers are inundated with ambiguous sustainability information. For example, greenwashing can make it difficult to identify environmentally friendly products. The highest-quality options, such as Life Cycle Assessment (LCA) scores or tailored impact certificates (e.g., environmentally friendly tags), designed for assessing the environmental impact of consumption, are ineffective in the setting of online shopping. They are simply too costly to provide a feasible solution when scaled up, and often rely on data from self-interested market players. We contribute an analysis of this online environment, exploring how the dynamic between sellers and consumers surfaces claims and concerns regarding sustainable consumption. In order to better provide information to consumers, we propose a machine learning method that can discover signals of sustainability from these interactions. Our method, SustainableSignals, is a first step in scaling up the provision of sustainability cues to online consumers.","doi":"10.24963/ijcai.2023/673","url":"https://ijcai.org/proceedings/2023/673","pdf":"https://ijcai.org/proceedings/2023/0673.pdf","markdown":"https://ijcai.org/proceedings/2023/673.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/673","volume":{"slug":"2023","title":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2023","url":"https://ijcai.org/proceedings/2023/","year":2023,"location":"Macao, SAR","dates":"19-25 August 2023","editors":"Edith Elkind","isbn":"978-1-956792-03-4","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2023"},"section":"AI for Good","track":"AI for Good","number":673,"pages":{"first":6067,"last":6075},"keywords":["AI for Good: Multidisciplinary Topics and Applications","AI for Good: Data Mining","AI for Good: Machine Learning"],"videos":[],"references":[{"position":1,"raw":"[Abnett, 2023] Kate Abnett. 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