Over the past few years, AI bias has become a central concern within design and computing fields. But as the concept of bias has grown in visibility, its meaning and form have become harder to grasp. To help designers realize bias, we take inspiration from textile bias (the skew of woven material) and examine the topic across its myriad forms: visual, textual, and tactile. By introducing a slanted experience of material and therefore of reality, we explore the translation of fraught machine learning algorithms into personal and probing artifacts. In this pictorial, we present nine pieces that materialize complex relationships with machine learning; ground these relationships in the present and the personal; and point to generative ways of engaging with biased systems around us.

Smart home cameras present new challenges for understanding behaviors and relationships surrounding always-on, domestic recording systems. We designed a series of discursive activities involving 16 individuals from ten households for six weeks in their everyday settings. These activities functioned as speculative probes—prompting participants to reflect on themes of privacy and power through filming with cameras in their households. Our research design foregrounded critical-playful enactments that allowed participants to speculate potentials for relationships with cameras in the home beyond everyday use. We present four key dynamics with participants and home cameras by examining their relationships to: the camera's eye, filming, their data, and camera's societal contexts. We contribute discussions about the mundane, information privacy, and post-hoc reflection with one's camera footage. Overall, our findings reveal the camera as a strange, yet banal entity in the home—interrogating how participants compose and handle their own and others’ video data.

The increased adoption of smart home cameras (SHCs) foregrounds issues of surveillance, power, and privacy in homes and neighborhoods. However, questions remain about how people are currently using these devices to monitor and surveil, what the benefits and limitations are for users, and what privacy and security tensions arise between primary users and other stakeholders. We present an empirical study with 14 SHC users to understand how these devices are used and integrated within everyday life. Based on semi-structured qualitative interviews, we investigate users’ motivations, practices, privacy concerns, and social negotiations. Our findings highlight the SHC as a perceptually powerful and spatially sensitive device that enables a variety of surveillant uses outside of basic home security—from formally surveilling domestic workers, to casually spying on neighbors, to capturing memories. We categorize surveillant SHC uses, clarify distinctions between primary and non-primary users, and highlight under-considered design directions for addressing power imbalances among primary and non-primary users.

The life cycle of a peer-produced dataset follows the phases of growth, maturity, and decline. Paying crowdworkers is a proven method to collect and organize information into structured tables. However, these tabular representations may contain inaccuracies due to errors or data changing over time. Thus, the maturation phase of a dataset can benefit from the additional human examination. One method to improve accuracy is to recruit additional paid crowdworkers to verify and correct errors. An alternative method relies on unpaid contributors, collectively editing the dataset during regular use. We describe two case studies to examine different strategies for human verification and maintenance of in-flux tabular datasets. The first case study examines traditional micro-task verification strategies with paid crowdworkers, while the second examines long-term maintenance strategies with unpaid contributions from non-crowdworkers. Two paid verification strategies that produced more accurate corrections at a lower cost per accurate correction were redundant data collection followed by final verification from a trusted crowdworker and allowing crowdworkers to review any data freely. In the unpaid maintenance strategies, contributors provided more accurate corrections when asked to review data matching their interests. This research identifies considerations and future approaches to collectively improving information accuracy and longevity of tabular information.