The question “what is a robot?” has long been contested as automated embodied systems encompass many forms. We examine this fundamental question in Human-Robot Interaction through the case of Major League Baseball’s “robot umpire,” officially known as the Automated Ball-Strike System (ABS). Drawing on the concept of “fluid technology,” we analyze how the robot umpire is not a fixed technological artifact but a fluid sociotechnical assemblage whose definition and function are continuously negotiated. Through ethnographic fieldwork and interviews with stakeholders across the baseball ecosystem, we demonstrate that the robot umpire’s physical boundaries, operational parameters, and authorship remain contested and evolving, shaped by ongoing interactions between technology developers, league officials, umpires, players, and fans. Our findings reveal that treating robots as fluid technologies—rather than as discrete objects—opens new possibilities for understanding human-robot relationships. We contribute both theoretical insights regarding the ontological flexibility of “robots” and methodological approaches for studying and designing robots as sociotechnical assemblages.

In this position paper, we argue that baseball-and sports more broadly-provide a unique and under-explored opportunity for researchers to study human-robot interaction (HRI) in real-world settings. Using the rise of robot umpires in baseball as a primary example, we examine emerging themes such as power dynamics among players and umpires, labor implications, and technical challenges. We emphasize the affordances and benefits of studying sports within HRI, including the integration of interdisciplinary perspectives, the large-scale deployment of robots, and the examination of their role in deeply rooted cultural practices.

The language used by US courtroom actors in criminal trials has long been studied for biases. However, systematic studies for bias in high-stakes court trials have been difficult, due to the nuanced nature of bias and the legal expertise required. Large language models offer the possibility to automate annotation. But validating the computational approach requires both an understanding of how automated methods fit in existing annotation workflows and what they really offer. We present a case study of adding a computational model to a complex and high-stakes problem: identifying gender-biased language in US capital trials for women defendants. Our team of experienced death-penalty lawyers and NLP technologists pursue a three-phase study: first annotating manually, then training and evaluating computational models, and finally comparing expert annotations to model predictions. Unlike many typical NLP tasks, annotating for gender bias in months-long capital trials is complicated, with many individual judgment calls. Contrary to standard arguments for automation that are based on efficiency and scalability, legal experts find the computational models most useful in providing opportunities to reflect on their own bias in annotation and to build consensus on annotation rules. This experience suggests that seeking to replace experts with computational models for complex annotation is both unrealistic and undesirable. Rather, computational models offer valuable opportunities to assist the legal experts in annotation-based studies.

Cross-lingual transfer learning is an important property of multilingual large language models (LLMs).But how do LLMs represent relationships between languages?Every language model has an input layer that maps tokens to vectors.This ubiquitous layer of language models is often overlooked.We find that similarities between these input embeddings are highly interpretable and that the geometry of these embeddings differs between model families.In one case (XLM-RoBERTa), embeddings encode language: tokens in different writing systems can be linearly separated with an average of 99.2% accuracy.Another family (mT5) represents cross-lingual semantic similarity: the 50 nearest neighbors for any token represent an average of 7.61 writing systems, and are frequently translations.This result is surprising given that there is no explicit parallel crosslingual training corpora and no explicit incentive for translations in pre-training objectives.Our research opens the door for investigations in 1) The effect of pre-training and model architectures on representations of languages and 2) The applications of cross-lingual representations embedded in language models.