This article describes the successful collaboration “in the wild” between Clinical Documentation Integrity Specialists (CDIS) and an Artificial Intelligence (AI)-embedded software to conduct knowledge work. CDIS review patient charts in near real-time to improve clinicians’ documentation, with the goal to make medical documentation more accurate, consistent and complete. CDIS collaborate with an AI-embedded “Computer Assisted Coding” (CAC) system that scans records from the Electronic Healthcare Record and auto-suggests codes based on natural language processing. CDIS find the CAC's suggestions are often inaccurate—often humorously so. Still, they find the CAC to be a useful helper, like Robin is to Batman. This human-AI collaboration is contingent on several factors: the flexible integration of the AI into the workflow similar to the notion of unremarkable AI; supporting the CDIS’ sensemaking; the CDIS’ knowledge about the CAC being predictably unreliable, an experience by the CDIS of the AI's value; humans remaining in control; and ability to experiment with the AI, which spurs reflection and learning for these knowledge workers.

Technology should be accessible and inclusive, so designers should learn to consider the needs of different users. Toward this end, we created the theoretically-grounded CIDER assumption elicitation technique, an educational analytical design evaluation method to teach inclusive design skills. CIDER (Critique,Imagine,Design,Expand,Repeat) helps designers recognize and respond to bias using the critical lens ofassumptions about users. Through an 11-week mixed-method case study in an interaction design course with 40 undergraduate students and follow-up interviews, we found that activities based on the CIDER technique may have helped students identify increasingly many types of design bias over time and reflect on their unconscious biases about users. The activities also had lasting impacts, encouraging some students to adopt more inclusive approaches in subsequent design work. We discuss the implications of these findings, namely that educational techniques like CIDER can help designers learn to create equitable technology designs.

The use of wearable sensor technology opens up exciting avenues for both art and HCI research, providing new ways to explore the invisible link between audience and performer. To be effective, such work requires close collaboration between performers and researchers. In this article, we report on the co-design process and research insights from our work integrating physiological sensing and live performance. We explore the connection between the audience’s physiological data and their experience during the performance, analyzing a multi-modal dataset collected from 98 audience members. We identify notable moments based on HRV and EDA, and show how the audience’s physiological responses can be linked to the choreography. The longitudinal changes in HRV features suggest a strong connection to the choreographer’s intended narrative arc, while EDA features appear to correspond with short-term audience responses to dramatic moments. We discuss the physiological phenomena and implications for designing feedback systems and interdisciplinary collaborations.

Data scientists require rich mental models of how AI systems behave to effectively train, debug, and work with them. Despite the prevalence of AI analysis tools, there is no general theory describing how people make sense of what their models have learned. We frame this process as a form of sensemaking and derive a framework describing how data scientists develop mental models of AI behavior. To evaluate the framework, we show how existing AI analysis tools fit into this sensemaking process and use it to design AIFinnity , a system for analyzing image-and-text models. Lastly, we explored how data scientists use a tool developed with the framework through a think-aloud study with 10 data scientists tasked with using AIFinnity to pick an image captioning model. We found that AIFinnity ’s sensemaking workflow reflected participants’ mental processes and enabled them to discover and validate diverse AI behaviors.

Optimal feedback control (OFC) is a theory from the motor control literature that explains how humans move their body to achieve a certain goal, e.g., pointing with the finger. OFC is based on the assumption that humans aim at controlling their body optimally, within the constraints imposed by body, environment, and task. In this article, we explain how this theory can be applied to understanding Human-Computer Interaction (HCI) in the case of pointing. We propose that the human body and computer dynamics can be interpreted as a single dynamical system. The system state is controlled by the user via muscle control signals, and estimated from observations. Between-trial variability arises from signal-dependent control noise and observation noise. We compare four different models from optimal control theory and evaluate to what degree these models can replicate movements in the case of mouse pointing. We introduce a procedure to identify parameters that best explain observed user behavior. To support HCI researchers in simulating, analyzing, and optimizing interaction movements, we provide the Python toolboxOFC4HCI. We conclude that OFC presents a powerful framework for HCI to understand and simulate motion of the human body and of the interface on a moment-by-moment basis.

This article investigates new relations with things that are expansive and inclusive of the pluralities and differences within our entanglements with technologies. We do this by extending our commitments to the methodological approaches of material speculation and co-speculation that led us to engage in multi-year conversations between ourselves as design researchers, philosophers, and a counterfactual artifact we designed, known as a Tilting Bowl. The philosophers lived with the Tilting Bowl during this period. We call these conversations, polylogues, of which the aim is to co-speculate on a range of new possible relations by which to consider living with technological things. The contributions of our article are two-fold. Firstly, through our polylogues, we offer descriptions of three relations with things. These include (1) non-anthropocentric care: care that is non-anthropocentric and existential; (2) non-presumptive relations: not-knowing in relating to and engaging with things; and (3) ideologized relations: ideologies that frame relations with technologies. Secondly, the article elaborates and critically reflects on co-speculation as a method for relational and situated knowing that can be of benefit to HCI researchers.

Automated decision-making systems become increasingly powerful due to higher model complexity. While powerful in prediction accuracy, Deep Learning models are black boxes by nature, preventing users from making informed judgments about the correctness and fairness of such an automated system. Explanations have been proposed as a general remedy to the black box problem. However, it remains unclear if effects of explanations on user trust generalise over varying accuracy levels. In an online user study with 959 participants, we examined the practical consequences of adding explanations for user trust: We evaluated trust for three explanation types on three classifiers of varying accuracy. We find that the influence of our explanations on trust differs depending on the classifier’s accuracy. Thus, the interplay between trust and explanations is more complex than previously reported. Our findings also reveal discrepancies between self-reported and behavioural trust, showing that the choice of trust measure impacts the results.

This article and the design fictions it presents are bound up with an ongoing qualitative-ethnographic study with Imazighen, the native people in remote Morocco. This group of people is marked by textual and digital illiteracy. We are in the process of developing multi-modal design fictions that can be used in workshops as a starting point for the co-development of further design fictions that envision the local population's desired digital futures. The design fictions take the form of storyboards, allowing for a non-textual engagement. The current content seeks to explore challenges, potentials, margins, and limitations for the future design of haptic and touch-sensitive technology as a means for interpersonal communication and information procurement. Design fictions provide a way of exposing the locals to possible digital futures so that they can actively engage with them and explore the bounds and confines of their literacy and the extent to which it matters.

Data analysis requires translating higher level questions and hypotheses into computable statistical models. We present a mixed-methods study aimed at identifying the steps, considerations, and challenges involved in operationalizing hypotheses into statistical models, a process we refer to as hypothesis formalization . In a formative content analysis of 50 research papers, we find that researchers highlight decomposing a hypothesis into sub-hypotheses, selecting proxy variables, and formulating statistical models based on data collection design as key steps. In a lab study, we find that analysts fixated on implementation and shaped their analyses to fit familiar approaches, even if sub-optimal. In an analysis of software tools, we find that tools provide inconsistent, low-level abstractions that may limit the statistical models analysts use to formalize hypotheses. Based on these observations, we characterize hypothesis formalization as a dual-search process balancing conceptual and statistical considerations constrained by data and computation and discuss implications for future tools.

Designing technologies that support the cybersecurity of older adults with memory concerns involves wrestling with an uncomfortable paradox between surveillance and independence and the close collaboration of couples. This research captures the interactions between older adult couples where one or both have memory concerns—a primary feature of cognitive decline—as they make decisions on how to safeguard their online activities using a Safety Setting probe we designed, and over the course of several informal interviews and a diary study. Throughout, couples demonstrated a collaborative mentality to which we apply a frame of citizenship in opensource collaboration, specifically (a) histories of participation , (b) lower barriers to participation, and (c) maintaining ongoing contribution. In this metaphor of collaborative enterprise, one partner (or member of the couple) may be the service provider and the other may be the participant, but at varying moments, they may switch roles while still maintaining a collaborative focus on preserving shared assets and freedom on the internet. We conclude with a discussion of what this service provider-contributor mentality means for empowerment through citizenship, and implications for vulnerable populations’ cybersecurity.