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Abstract Knowledge of the behaviour of marine‐based ice sheets during times of climatic warming, such as the last deglaciation, provides important information to understand how ice sheets respond to external forcing. We analysed swath bathymetric and acoustic sub‐bottom profiler data from Wrigley Gulf on the western Amundsen Sea shelf, West Antarctica, to identify glacial features and reconstruct past changes in the extent of the West Antarctic Ice Sheet (WAIS) and ice flow directions. Glacial bedforms mapped within a bathymetric cross‐shelf trough include features showing cross‐cutting and overprinting relationship and indicate changes in ice‐flow orientation. Here, we distinguish at least two phases of different ice‐flow patterns on the Wrigley Gulf shelf. During the earlier phase, seaward ice stream flow on the inner shelf was deflected towards the east due to the existence of an ice dome on the middle‐outer continental shelf. Retreat of grounded ice towards the centre of this dome is indicated by the asymmetric cross profile of recessional moraines mapped on the middle shelf. The later glaciation phase was characterized by fast, NNW‐directed ice flow across the shelf along a broad front and subsequent stepwise landward retreat, which is evident from the common occurrence and orientation of mega‐scale glaciation lineations and grounding zone wedges on the middle‐inner shelf. It is uncertain whether the two phases of glaciation recorded on the seafloor occurred during the last and penultimate glacial periods or at different times of the last glaciation. Reliable chronological constraints from sediment cores and additional geomorphological information are needed to understand the cause of the changes in WAIS dynamics reflected by the two ice‐flow phases. 

Research exploring how to support decision-making has often used machine learning to automate or assist human decisions. We take an alternative approach for improving decision-making, using machine learning to help stakeholders surface ways to improve and make fairer decision-making processes. We created "Deliberating with AI", a web tool that enables people to create and evaluate ML models in order to examine strengths and shortcomings of past decision-making and deliberate on how to improve future decisions. We apply this tool to a context of people selection, having stakeholders---decision makers (faculty) and decision subjects (students)---use the tool to improve graduate school admission decisions. Through our case study, we demonstrate how the stakeholders used the web tool to create ML models that they used as boundary objects to deliberate over organization decision-making practices. We share insights from our study to inform future research on stakeholder-centered participatory AI design and technology for organizational decision-making.