Recommender systems (RS), which are widely deployed across high-stakes domains, are susceptible to biases that can cause large-scale societal impacts. Researchers have proposed methods to measure and mitigate such biases—but translating academic theory into practice is inherently challenging. Through a semi-structured interview study (N=11), we map the RS practitioner workflow within large technology companies, focusing on how technical teams consider fairness internally and in collaboration with legal, data, and fairness teams. We identify key challenges to incorporating fairness into existing RS workflows: defining fairness in RS contexts, balancing multi-stakeholder interests, and navigating dynamic environments. We also identify key organization-wide challenges: making time for fairness work and facilitating cross-team communication. Finally, we offer actionable recommendations for the RS community, including practitioners and HCI researchers.

Motion capture systems, used across various domains, make body representations concrete through technical processes. We argue that the measurement of bodies and the validation of measurements for motion capture systems can be understood as social practices. By analyzing the findings of a systematic literature review (N=278) through the lens of social practice theory, we show how these practices, and their varying attention to errors, become ingrained in motion capture design and innovation over time. Moreover, we show how contemporary motion capture systems perpetuate assumptions about human bodies and their movements. We suggest that social practices of measurement and validation are ubiquitous in the development of data- and sensor-driven systems more broadly, and provide this work as a basis for investigating hidden design assumptions and their potential negative consequences in human-computer interaction.

SMS scams have surged over the recent years. However, little empirical research has been done to understand this rising threat due to the lack of an updated dataset. In the UK, mobile network operators run a firewall to block illicit messages. To this end, we collaborate with a major UK mobile network operator, which provides us with 3.58m SMS messages flagged by their firewall. These messages originated from over 42k unique sender IDs and were sent to 2.23m mobile numbers between December 2023 and February 2024. This is the first research to examine the current threats in the SMS ecosystem and categorize illicit SMS messages into eight sectors, including spam. We present the distribution of SMS messages successfully blocked by the mobile network operator's firewall and those that successfully evade detection.